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  <doc>
    <id>2731</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1911</pageFirst>
    <pageLast>1927</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>14 (2018)</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Loss of the novel Vcp (valosin containing protein) interactor Washc4 interferes with autophagy-mediated proteostasis in striated muscle and leads to myopathy in vivo</title>
    <abstract language="eng">VCP/p97 (valosin containing protein) is a key regulator of cellular proteostasis. It orchestrates protein turnover and quality control in vivo, processes fundamental for proper cell function. In humans, mutations in VCP lead to severe myo- and neuro-degenerative disorders such as inclusion body myopathy with Paget disease of the bone and frontotemporal dementia (IBMPFD), amyotrophic lateral sclerosis (ALS) or and hereditary spastic paraplegia (HSP). We analyzed here the in vivo role of Vcp and its novel interactor Washc4/Swip (WASH complex subunit 4) in the vertebrate model zebrafish (Danio rerio). We found that targeted inactivation of either Vcp or Washc4, led to progressive impairment of cardiac and skeletal muscle function, structure and cytoarchitecture without interfering with the differentiation of both organ systems. Notably, loss of Vcp resulted in compromised protein degradation via the proteasome and the macroautophagy/autophagy machinery, whereas Washc4 deficiency did not affect the function of the ubiquitin-proteasome system (UPS) but caused ER stress and interfered with autophagy function in vivo. In summary, our findings provide novel insights into the in vivo functions of Vcp and its novel interactor Washc4 and their particular and distinct roles during proteostasis in striated muscle cells.</abstract>
    <parentTitle language="eng">Autophagy</parentTitle>
    <identifier type="issn">1554-8627</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27311</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1080/15548627.2018.1491491</enrichment>
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    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Monika</first_name>
      <last_name>Kustermann</last_name>
    </author>
    <author>
      <first_name>Linda</first_name>
      <last_name>Manta</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Paone</last_name>
    </author>
    <author>
      <first_name>Jochen</first_name>
      <last_name>Kustermann</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Cora</first_name>
      <last_name>Wiesner</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Eichinger</last_name>
    </author>
    <author>
      <first_name>Christoph S.</first_name>
      <last_name>Clemen</last_name>
    </author>
    <author>
      <first_name>Rolf</first_name>
      <last_name>Schröder</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Sandri</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Rottbauer</last_name>
    </author>
    <author>
      <first_name>Steffen</first_name>
      <last_name>Just</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Proteostasis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>striated muscle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vcp</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Washc4</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>zebrafish</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2731/Loss_of_the_novel_Vcp_interactor.pdf</file>
  </doc>
  <doc>
    <id>2723</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>463</pageFirst>
    <pageLast>480</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>15</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data</title>
    <abstract language="eng">Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.</abstract>
    <parentTitle language="eng">Advances in Data Analysis and Classification</parentTitle>
    <identifier type="issn">1862-5355</identifier>
    <identifier type="issn">1862-5347</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27235</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>André</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Adalbert</first_name>
      <last_name>Wilhelm</last_name>
    </author>
    <author>
      <first_name>Timo</first_name>
      <last_name>Ropinski</last_name>
    </author>
    <author>
      <first_name>Matthias</first_name>
      <last_name>Platzer</last_name>
    </author>
    <author>
      <first_name>Heiko</first_name>
      <last_name>Neumann</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>colour scales</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>bivariate</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visualisation</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2723/s11634-020-00416-5.pdf</file>
  </doc>
  <doc>
    <id>5303</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>353</pageFirst>
    <pageLast>363</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>13</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Hoboken</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-11-05</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Combined analysis of a serum mRNA/miRNA marker signature and CA 19‐9 for timely and accurate diagnosis of recurrence after resection of pancreatic ductal adenocarcinoma: A prospective multicenter cohort study</title>
    <abstract language="eng">Background and Aims&#13;
&#13;
Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples.&#13;
&#13;
Methods&#13;
&#13;
In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a “No Evidence of Disease” and a “Recurrence” group based on clinical follow‐up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7‐gene marker signature with CA19‐9 data.&#13;
&#13;
Results&#13;
&#13;
The best‐performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19‐9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow‐up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures.&#13;
&#13;
Conclusions&#13;
&#13;
TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow‐up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work‐up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need.</abstract>
    <parentTitle language="eng">United European Gastroenterology Journal</parentTitle>
    <identifier type="issn">2050-6414</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-53036</identifier>
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This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Methods&lt;\/jats:title&gt;&lt;jats:p&gt;In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a \u201cNo Evidence of Disease\u201d and a \u201cRecurrence\u201d group based on clinical follow\u2010up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). 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    <author>
      <first_name>Malte</first_name>
      <last_name>Buchholz</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Miriam</first_name>
      <last_name>Schenk</last_name>
    </author>
    <author>
      <first_name>Julie</first_name>
      <last_name>Earl</last_name>
    </author>
    <author>
      <first_name>Rita T.</first_name>
      <last_name>Lawlor</last_name>
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    <author>
      <first_name>Aldo</first_name>
      <last_name>Scarpa</last_name>
    </author>
    <author>
      <first_name>Alfonso</first_name>
      <last_name>Sanjuanbenito</last_name>
    </author>
    <author>
      <first_name>Alfredo</first_name>
      <last_name>Carrato</last_name>
    </author>
    <author>
      <first_name>Nuria</first_name>
      <last_name>Malats</last_name>
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    <author>
      <first_name>Christine</first_name>
      <last_name>Tjaden</last_name>
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    <author>
      <first_name>Nathalia A.</first_name>
      <last_name>Giese</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Büchler</last_name>
    </author>
    <author>
      <first_name>Thilo</first_name>
      <last_name>Hackert</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Thomas M.</first_name>
      <last_name>Gress</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5303/Buchholz_Combined.pdf</file>
  </doc>
  <doc>
    <id>2730</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>8 (2018)</volume>
    <articleNumber>158</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species</title>
    <abstract language="eng">Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats.</abstract>
    <parentTitle language="eng">Biomolecules</parentTitle>
    <identifier type="issn">2218-273X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27303</identifier>
    <note>This article belongs to the Special Issue "Biomolecules for Translational Approaches in Cardiology"</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/biom8040158</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Lea</first_name>
      <last_name>Kubis</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Rottbauer</last_name>
    </author>
    <author>
      <first_name>Derk</first_name>
      <last_name>Frank</last_name>
    </author>
    <author>
      <first_name>Steffen</first_name>
      <last_name>Just</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>heart failure phenotypes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>zebrafish</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wistar rat</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>semantic multi-classifier systems</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2730/biomolecules-08-00158-v2.pdf</file>
  </doc>
  <doc>
    <id>2732</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue/>
    <volume>19 (2018)</volume>
    <articleNumber>390</articleNumber>
    <type>article</type>
    <publisherName>BioMed Central</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">3D Network exploration and visualisation for lifespan data</title>
    <abstract language="eng">Background&#13;
&#13;
The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration.&#13;
&#13;
Results&#13;
&#13;
Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results.&#13;
&#13;
Conclusion&#13;
&#13;
Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.</abstract>
    <parentTitle language="eng">BMC Bioinformatics</parentTitle>
    <identifier type="issn">1471-2105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27327</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1186/s12859-018-2393-x</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Rolf</first_name>
      <last_name>Hühne</last_name>
    </author>
    <author>
      <first_name>Viktor</first_name>
      <last_name>Kessler</last_name>
    </author>
    <author>
      <first_name>Axel</first_name>
      <last_name>Fürstberger</last_name>
    </author>
    <author>
      <first_name>Silke D.</first_name>
      <last_name>Werle</last_name>
    </author>
    <author>
      <first_name>Matthias</first_name>
      <last_name>Platzer</last_name>
    </author>
    <author>
      <first_name>Jürgen</first_name>
      <last_name>Sühnel</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lifespan</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ageing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gene network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>3D visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ageing factor database</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AgeFactDB</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>differentially expressed genes</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2732/s12859-018-2393-x.pdf</file>
  </doc>
  <doc>
    <id>2733</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>37</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>4 (2018)</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>KIT Scientific Publishing</publisherName>
    <publisherPlace>Karlsruhe</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Ordinal Classifiers Can Fail on Repetitive Class Structures</title>
    <abstract language="eng">Ordinal classifiers are constrained classification algorithms that assume a predefined (total) order of the class labels to be reflected in the feature space of a dataset. This information is used to guide the training of ordinal classifiers and might lead to an improved classification performance. Incorrect assumptions on the order of a dataset can result in diminished detection rates. Ordinal classifiers can, therefore, be used to screen for ordinal class structures within a feature representation. While it was shown that algorithms could in principle reject incorrect class orderings, it is unclear if all remaining candidate orders reflect real ordinal structures in feature space. In this work we characterize the decision regions induced by ordinal classifiers. We show that they can fulfill different criteria that might be considered as ordinal reflections. These criteria are mainly determined by the connectedness and the neighborhood of the decision regions. We evaluate them for ordinal classifier cascades constructed from binary classifiers. We show that depending on the type of base classifier they bear the risk of not rejecting non ordinal, like partial repetitive, structures.</abstract>
    <parentTitle language="eng">Archives of Data Science, Series A</parentTitle>
    <identifier type="issn">2363-9881</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27335</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.5445/KSP/1000085951/25</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-SA 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Lisa M.</first_name>
      <last_name>Schäfer</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2733/Lausser.pdf</file>
  </doc>
  <doc>
    <id>2735</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>9</volume>
    <articleNumber>11746</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessing phenotype order in molecular data</title>
    <abstract language="eng">Biological entities are key elements of biomedical research. Their definition and their relationships are important in areas such as phylogenetic reconstruction, developmental processes or tumor evolution. Hypotheses about relationships like phenotype order are often postulated based on prior knowledge or belief. Evidence on a molecular level is typically unknown and whether total orders are reflected in the molecular measurements is unclear or not assessed. In this work we propose a method that allows a fast and exhaustive screening for total orders in large datasets. We utilise ordinal classifier cascades to identify discriminable molecular representations of the phenotypes. These classifiers are constrained by an order hypothesis and are highly sensitive to incorrect assumptions. Two new error bounds, which are introduced and theoretically proven, lead to a substantial speed-up and allow the application to large collections of many phenotypes. In our experiments we show that by exhaustively evaluating all possible candidate orders, we are able to identify phenotype orders that best coincide with the high-dimensional molecular profiles.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27353</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-019-48150-z</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Lisa M.</first_name>
      <last_name>Schäfer</last_name>
    </author>
    <author>
      <first_name>Lyn-Rouven</first_name>
      <last_name>Schirra</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Szekely</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>systems biology</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2735/s41598-019-48150-z.pdf</file>
  </doc>
  <doc>
    <id>2736</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>163</issue>
    <volume>17 (2020)</volume>
    <articleNumber>20190612</articleNumber>
    <type>article</type>
    <publisherName>The Royal Society</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Constraining classifiers in molecular analysis</title>
    <abstract language="eng">Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.</abstract>
    <parentTitle language="eng">Journal of the Royal Society Interface</parentTitle>
    <subTitle language="eng">invariance and robustness</subTitle>
    <identifier type="issn">1742-5662</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27361</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1098/rsif.2019.0612</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Szekely</last_name>
    </author>
    <author>
      <first_name>Attila</first_name>
      <last_name>Klimmek</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computational learning theory</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>invariances</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>molecular profiles</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2736/rsif.2019.0612.pdf</file>
  </doc>
  <doc>
    <id>2718</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>23</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>12 (2022)</volume>
    <articleNumber>1815</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Theoretical Approach to Ordinal Classification</title>
    <abstract language="eng">Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a common mathematical definition for OC tasks. More precisely, in general, a classification task is defined as an OC task, solely based on the corresponding class label names. However, an ordinal class structure that is identified based on the class labels is not necessarily reflected in the corresponding feature space. In contrast, naturally any kind of multi-class classification task can consist of a set of arbitrary class labels that form an ordinal structure which can be observed in the current feature space. Based on this simple observation, in this work, we present our generalised approach towards an intuitive working definition for OC tasks, which is based on the corresponding feature space and allows a classifier-independent detection of ordinal class structures. To this end, we introduce and discuss novel, OC-specific theoretical concepts. Moreover, we validate our proposed working definition in combination with a set of traditionally ordinal and traditionally non-ordinal data sets, and provide the results of the corresponding detection algorithm. Additionally, we motivate our theoretical concepts, based on an illustrative evaluation of one of the oldest and most popular machine learning data sets, i.e., on the traditionally non-ordinal Fisher’s Iris data set.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <subTitle language="eng">Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures</subTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27186</identifier>
    <note>This article belongs to the Section "Computing and Artificial Intelligence"</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/app12041815</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Peter</first_name>
      <last_name>Bellmann</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Friedhelm</first_name>
      <last_name>Schwenker</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ordinal classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>detection of ordinal class structures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Fisher’s discriminant ratio</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2718/applsci-12-01815-v3.pdf</file>
  </doc>
  <doc>
    <id>2719</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3530</pageFirst>
    <pageLast>3537</pageLast>
    <pageNumber/>
    <edition/>
    <issue>20</issue>
    <volume>37</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Oxford University Press</publisherName>
    <publisherPlace>Oxford</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Capturing dynamic relevance in Boolean networks using graph theoretical measures</title>
    <abstract language="eng">Motivation&#13;
&#13;
Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology.&#13;
&#13;
Results&#13;
&#13;
Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible.</abstract>
    <parentTitle language="eng">Bioinformatics</parentTitle>
    <identifier type="issn">1460-2059</identifier>
    <identifier type="issn">1367-4803</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27194</identifier>
    <note>Supplementary data are available at Bioinformatics online.</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1093/bioinformatics/btab277</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/sysbio-bioinf/BNStatic</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Felix M.</first_name>
      <last_name>Weidner</last_name>
    </author>
    <author>
      <first_name>Julian D.</first_name>
      <last_name>Schwab</last_name>
    </author>
    <author>
      <first_name>Silke D.</first_name>
      <last_name>Werle</last_name>
    </author>
    <author>
      <first_name>Nensi</first_name>
      <last_name>Ikonomi</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2719/btab277.pdf</file>
  </doc>
  <doc>
    <id>2721</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>13</volume>
    <articleNumber>e12262</articleNumber>
    <type>article</type>
    <publisherName>Alzheimer’s Association</publisherName>
    <publisherPlace>Chicago</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Predicting disease progression in behavioral variant frontotemporal dementia</title>
    <abstract language="eng">Introduction&#13;
&#13;
The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.&#13;
&#13;
Methods&#13;
&#13;
One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models.&#13;
&#13;
Results&#13;
&#13;
We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models.&#13;
&#13;
Discussion&#13;
&#13;
Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.</abstract>
    <parentTitle language="eng">Alzheimer’s &amp; Dementia: Diagnosis, Assessment &amp; Disease Monitoring</parentTitle>
    <identifier type="issn">2352-8729</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27212</identifier>
    <note>Collaborators&#13;
&#13;
FTLD consortium: Christine v. Arnim, Petra Steinacker, Georg Grön, Manuel Maler, Maryna Polyakova, Christopher Weise, Lea Hüper, Daniele Pino, Annerose Engel, Johannes Levin, Catharina Prix, Elisabeth Wlasich, Lina Riedl, Felix Müller-Sarnowski, Carola Roßmeier, Felix Oberhauser, Kai Schumacher, Jan Lehmbeck, Thomas Duning, Andreas Johnen.</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1002/dad2.12262</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Sarah</first_name>
      <last_name>Anderl-Straub</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Jolina</first_name>
      <last_name>Lombardi</last_name>
    </author>
    <author>
      <first_name>Ingo</first_name>
      <last_name>Uttner</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Fassbender</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Fliessbach</last_name>
    </author>
    <author>
      <first_name>Hans-Jürgen</first_name>
      <last_name>Huppertz</last_name>
    </author>
    <author>
      <first_name>Holger</first_name>
      <last_name>Jahn</last_name>
    </author>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Kornhuber</last_name>
    </author>
    <author>
      <first_name>Hellmuth</first_name>
      <last_name>Obrig</last_name>
    </author>
    <author>
      <first_name>Anja</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Elisa</first_name>
      <last_name>Semler</last_name>
    </author>
    <author>
      <first_name>Matthis</first_name>
      <last_name>Synofzik</last_name>
    </author>
    <author>
      <first_name>Adrian</first_name>
      <last_name>Danek</last_name>
    </author>
    <author>
      <first_name>Johannes</first_name>
      <last_name>Prudlo</last_name>
    </author>
    <author>
      <first_name>Jan</first_name>
      <last_name>Kassubek</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Landwehrmeyer</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Lauer</last_name>
    </author>
    <author>
      <first_name>Alexander E.</first_name>
      <last_name>Volk</last_name>
    </author>
    <author>
      <first_name>Jens</first_name>
      <last_name>Wiltfang</last_name>
    </author>
    <author>
      <first_name>Janine</first_name>
      <last_name>Diehl-Schmid</last_name>
    </author>
    <author>
      <first_name>Albert C.</first_name>
      <last_name>Ludolph</last_name>
    </author>
    <author>
      <first_name>Matthias</first_name>
      <last_name>Schroeter</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Otto</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>behavioral variant frontotemporal dementia</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>brain volume</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classification models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>disease progression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>frontotemporal dementia</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>prognosis</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2721/Alz-Dem_Anderl-Straub_2022.pdf</file>
  </doc>
  <doc>
    <id>2722</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume>8</volume>
    <articleNumber>671274</articleNumber>
    <type>article</type>
    <publisherName>Frontiers</publisherName>
    <publisherPlace>Lausanne</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">NADH Fluorescence Lifetime Imaging Microscopy Reveals Selective Mitochondrial Dysfunction in Neurons Overexpressing Alzheimer’s Disease–Related Proteins</title>
    <abstract language="eng">Alzheimer’s disease (AD), the most prevalent form of dementia, affects globally more than 30 million people suffering from cognitive deficits and neuropsychiatric symptoms. Substantial evidence for the involvement of mitochondrial dysfunction in the development and/or progression of AD has been shown in addition to the pathological hallmarks amyloid beta (Aβ) and tau. Still, the selective vulnerability and associated selective mitochondrial dysfunction cannot even be resolved to date. We aimed at optically quantifying mitochondrial function on a single-cell level in primary hippocampal neuron models of AD, unraveling differential involvement of cell and mitochondrial populations in amyloid precursor protein (APP)-associated mitochondrial dysfunction. NADH lifetime imaging is a highly sensitive marker-free method with high spatial resolution. However, deciphering cellular bioenergetics of complex cells like primary neurons has still not succeeded yet. To achieve this, we combined highly sensitive NADH lifetime imaging with respiratory inhibitor treatment, allowing characterization of mitochondrial function down to even the subcellular level in primary neurons. Measuring NADH lifetime of the same neuron before and after respiratory treatment reveals the metabolic delta, which can be taken as a surrogate for cellular redox capacity. Correlating NADH lifetime delta with overexpression strength of Aβ-related proteins on the single-cell level, we could verify the important role of intracellular Aβ-mediated mitochondrial toxicity. Subcellularly, we could demonstrate a higher respiration in neuronal somata in general than dendrites, but a similar impairment of somatic and dendritic mitochondria in our AD models. This illustrates the power of NADH lifetime imaging in revealing mitochondrial function on a single and even subcellular level and its potential to shed light into bioenergetic alterations in neuropsychiatric diseases and beyond.</abstract>
    <parentTitle language="eng">Frontiers in Molecular Biosciences</parentTitle>
    <identifier type="issn">2296-889X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27224</identifier>
    <note>Section: Molecular Diagnostics and Therapeutics</note>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3389/fmolb.2021.671274</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Moritz</first_name>
      <last_name>Niederschweiberer</last_name>
    </author>
    <author>
      <first_name>Patrick M.</first_name>
      <last_name>Schaefer</last_name>
    </author>
    <author>
      <first_name>Larry N.</first_name>
      <last_name>Singh</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Devyani</first_name>
      <last_name>Bhosale</last_name>
    </author>
    <author>
      <first_name>Raphael</first_name>
      <last_name>Hesse</last_name>
    </author>
    <author>
      <first_name>Enrico</first_name>
      <last_name>Calzia</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Angelika</first_name>
      <last_name>Rueck</last_name>
    </author>
    <author>
      <first_name>Douglas</first_name>
      <last_name>Wallace</last_name>
    </author>
    <author>
      <first_name>Bjoern von</first_name>
      <last_name>Einem</last_name>
    </author>
    <author>
      <first_name>Christine A. F. von</first_name>
      <last_name>Arnim</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mitochondria</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy metabolism</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Alzheimer’s disease</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NADH</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>redox imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>amyloid beta</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2722/fmolb-08-671274.pdf</file>
  </doc>
  <doc>
    <id>2724</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>241</pageFirst>
    <pageLast>249</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>6 (2018)</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Big data and precision medicine</title>
    <parentTitle language="eng">International Journal of Data Science and Analytics</parentTitle>
    <subTitle language="eng">challenges and strategies with healthcare data</subTitle>
    <identifier type="issn">2364-4168</identifier>
    <identifier type="issn">2364-415X</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s41060-018-0095-0</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Johann M.</first_name>
      <last_name>Kraus</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Peter</first_name>
      <last_name>Kuhn</last_name>
    </author>
    <author>
      <first_name>Franz</first_name>
      <last_name>Jobst</last_name>
    </author>
    <author>
      <first_name>Michaela</first_name>
      <last_name>Bock</last_name>
    </author>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Halanke</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Hummel</last_name>
    </author>
    <author>
      <first_name>Peter</first_name>
      <last_name>Heuschmann</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>big data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>precision medicine</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>health care</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data science</value>
    </subject>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
  </doc>
  <doc>
    <id>2725</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue>304</issue>
    <volume>(2018)</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">sAPPβ and sAPPα increase structural complexity and E/I input ratio in primary hippocampal neurons and alter Ca2+ homeostasis and CREB1-signaling</title>
    <parentTitle language="eng">Experimental Neurology</parentTitle>
    <identifier type="issn">0014-4886</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.expneurol.2018.02.010</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Raphael</first_name>
      <last_name>Hesse</last_name>
    </author>
    <author>
      <first_name>Bjoern von</first_name>
      <last_name>Einem</last_name>
    </author>
    <author>
      <first_name>Franziska</first_name>
      <last_name>Wagner</last_name>
    </author>
    <author>
      <first_name>Patricia</first_name>
      <last_name>Bott</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Schwanzar</last_name>
    </author>
    <author>
      <first_name>Rosemary</first_name>
      <last_name>Jackson</last_name>
    </author>
    <author>
      <first_name>Karl Josef</first_name>
      <last_name>Föhr</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Katja S.</first_name>
      <last_name>Kroker</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Proepper</last_name>
    </author>
    <author>
      <first_name>Paul</first_name>
      <last_name>Walther</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Tara</first_name>
      <last_name>Spires-Jones</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Boeckers</last_name>
    </author>
    <author>
      <first_name>Holger</first_name>
      <last_name>Rosenbrock</last_name>
    </author>
    <author>
      <first_name>Christine A. F. von</first_name>
      <last_name>Arnim</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sAPP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AD</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neuronal plasticity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neurodevelopment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CREB-1 signaling</value>
    </subject>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
  </doc>
  <doc>
    <id>2753</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>871</pageFirst>
    <pageLast>884</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>14</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Chained correlations for feature selection</title>
    <abstract language="eng">Data-driven algorithms stand and fall with the availability and quality of existing data sources. Both can be limited in high-dimensional settings (n≫m). For example, supervised learning algorithms designed for molecular pheno- or genotyping are restricted to samples of the corresponding diagnostic classes. Samples of other related entities, such as arise in differential diagnosis, are usually not utilized in this learning scheme. Nevertheless, they might provide domain knowledge on the background or context of the original diagnostic task. In this work, we discuss the possibility of incorporating samples of foreign classes in the training of diagnostic classification models that can be related to the task of differential diagnosis. Especially in heterogeneous data collections comprising multiple diagnostic categories, the foreign ones can change the magnitude of available samples. More precisely, we utilize this information for the internal feature selection process of diagnostic models. We propose the use of chained correlations of original and foreign diagnostic classes. This method allows the detection of intermediate foreign classes by evaluating the correlation between class labels and features for each pair of original and foreign categories. Interestingly, this criterion does not require direct comparisons of the initial diagnostic groups and therefore, might be suitable for settings with restricted data access.</abstract>
    <parentTitle language="eng">Advances in Data Analysis and Classification</parentTitle>
    <identifier type="issn">1862-5355</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27534</identifier>
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    <enrichment key="THI_DownloadUrl">https://doi.org/10.1007/s11634-020-00397-5</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Szekely</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>feature selection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>high-dimensional data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>differential diagnosis</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2753/s11634-020-00397-5.pdf</file>
  </doc>
  <doc>
    <id>2755</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2583</pageFirst>
    <pageLast>2605</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>52</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Science+Business Media</publisherName>
    <publisherPlace>Dordrecht</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Detecting Ordinal Subcascades</title>
    <abstract language="eng">Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.</abstract>
    <parentTitle language="eng">Neural Processing Letters</parentTitle>
    <identifier type="issn">1573-773X</identifier>
    <identifier type="issn">1370-4621</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27555</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s11063-020-10362-0</enrichment>
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    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Lisa M.</first_name>
      <last_name>Schäfer</last_name>
    </author>
    <author>
      <first_name>Silke D.</first_name>
      <last_name>Werle</last_name>
    </author>
    <author>
      <first_name>Angelika M. R.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ordinal classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classifier cascades</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>error bounds</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>subsets</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>supersets</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2755/s11063-020-10362-0.pdf</file>
  </doc>
  <doc>
    <id>3029</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue/>
    <volume>12</volume>
    <articleNumber>21485</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-12-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient cross-validation traversals in feature subset selection</title>
    <abstract language="eng">Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets O(nm2) to those of one-dimensional ones O(m2). Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-30299</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-022-25942-4</enrichment>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Szekely</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Maucher</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
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    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3029/s41598-022-25942-4.pdf</file>
  </doc>
  <doc>
    <id>3807</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>9</volume>
    <articleNumber>22</articleNumber>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective</title>
    <abstract language="eng">AbstractPancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.</abstract>
    <parentTitle language="eng">npj Systems Biology and Applications</parentTitle>
    <identifier type="issn">2056-7189</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-38071</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Silke D.</first_name>
      <last_name>Werle</last_name>
    </author>
    <author>
      <first_name>Nensi</first_name>
      <last_name>Ikonomi</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <author>
      <first_name>Annika M. T. U.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Felix M.</first_name>
      <last_name>Weidner</last_name>
    </author>
    <author>
      <first_name>Julian D.</first_name>
      <last_name>Schwab</last_name>
    </author>
    <author>
      <first_name>Julia</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Malte</first_name>
      <last_name>Buchholz</last_name>
    </author>
    <author>
      <first_name>Thomas M.</first_name>
      <last_name>Gress</last_name>
    </author>
    <author>
      <first_name>Angelika M. R.</first_name>
      <last_name>Kestler</last_name>
    </author>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
    </author>
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    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/3807/s41540-023-00283-8.pdf</file>
  </doc>
  <doc>
    <id>3887</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
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    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identifying Key Features in Aggregated Real-Life AIS-Messages for Classification of Maritime Vessels by Random Forests</title>
    <parentTitle language="eng">2023 26th International Conference on Information Fusion (FUSION)</parentTitle>
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    <enrichment key="conference_title">2023 26th International Conference on Information Fusion (FUSION)</enrichment>
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    <author>
      <first_name>Max</first_name>
      <last_name>Krüger</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>key features</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AIS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>aggregated messages</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>performance evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>experimental comparison</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>maritime surveillance systems</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="persons" number="44767">Krüger, Max</collection>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
  </doc>
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    <id>3112</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>66</pageFirst>
    <pageLast>77</pageLast>
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    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Selecting Features from Foreign Classes</title>
    <parentTitle language="deu">Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018: Proceedings</parentTitle>
    <identifier type="isbn">978-3-319-99978-4</identifier>
    <identifier type="isbn">978-3-319-99977-7</identifier>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Luca</first_name>
      <last_name>Pancioni</last_name>
    </editor>
    <author>
      <first_name>Robin</first_name>
      <last_name>Szekely</last_name>
    </author>
    <editor>
      <first_name>Friedhelm</first_name>
      <last_name>Schwenker</last_name>
    </editor>
    <author>
      <first_name>Viktor</first_name>
      <last_name>Kessler</last_name>
    </author>
    <editor>
      <first_name>Edmondo</first_name>
      <last_name>Trentin</last_name>
    </editor>
    <author>
      <first_name>Friedhelm</first_name>
      <last_name>Schwenker</last_name>
    </author>
    <collection role="persons" number="49455">Lausser, Ludwig</collection>
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  <doc>
    <id>3113</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>55</pageFirst>
    <pageLast>64</pageLast>
    <pageNumber/>
    <edition/>
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    <title language="eng">Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades</title>
    <parentTitle language="eng">Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021: Proceedings</parentTitle>
    <identifier type="isbn">978-3-030-73973-7</identifier>
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    <author>
      <first_name>Peter</first_name>
      <last_name>Bellmann</last_name>
    </author>
    <editor>
      <first_name>Andrea</first_name>
      <last_name>Torsello</last_name>
    </editor>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Luca</first_name>
      <last_name>Rossi</last_name>
    </editor>
    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
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    <editor>
      <first_name>Marcello</first_name>
      <last_name>Pelillo</last_name>
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    <editor>
      <first_name>Battista</first_name>
      <last_name>Biggio</last_name>
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    <editor>
      <first_name>Antonio</first_name>
      <last_name>Robles-Kelly</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ordinal classification</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>Ordinal classifier cascades</value>
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    <title language="eng">Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario</title>
    <parentTitle language="eng">Pattern Recognition: ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021: Proceedings, Part VI</parentTitle>
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      <last_name>Bellmann</last_name>
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    <editor>
      <first_name>Alberto</first_name>
      <last_name>Del Bimbo</last_name>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
    </author>
    <editor>
      <first_name>Rita</first_name>
      <last_name>Cucchiara</last_name>
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    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
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    <editor>
      <first_name>Stan</first_name>
      <last_name>Sclaroff</last_name>
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    <author>
      <first_name>Friedhelm</first_name>
      <last_name>Schwenker</last_name>
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    <editor>
      <first_name>Giovanni Maria</first_name>
      <last_name>Farinella</last_name>
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    <editor>
      <first_name>Tao</first_name>
      <last_name>Mei</last_name>
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    <editor>
      <first_name>Marco</first_name>
      <last_name>Bertini</last_name>
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    <editor>
      <first_name>Hugo Jair</first_name>
      <last_name>Escalante</last_name>
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      <first_name>Roberto</first_name>
      <last_name>Vezzani</last_name>
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      <value>Ordinal classifier cascades</value>
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      <value>Ordinal classification</value>
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      <language>eng</language>
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      <value>Pain intensity recognition</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>Physiological signals</value>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
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    <volume>6</volume>
    <articleNumber>1056422</articleNumber>
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    <title language="eng">Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images</title>
    <abstract language="eng">In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.</abstract>
    <parentTitle language="eng">Frontiers in Artificial Intelligence</parentTitle>
    <identifier type="issn">2624-8212</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-32108</identifier>
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    <author>
      <first_name>Patrick</first_name>
      <last_name>Thiam</last_name>
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    <author>
      <first_name>Ludwig</first_name>
      <last_name>Lausser</last_name>
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    <author>
      <first_name>Christopher</first_name>
      <last_name>Kloth</last_name>
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      <first_name>Daniel</first_name>
      <last_name>Blaich</last_name>
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      <first_name>Andreas</first_name>
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      <first_name>Meinrad</first_name>
      <last_name>Beer</last_name>
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    <author>
      <first_name>Hans A.</first_name>
      <last_name>Kestler</last_name>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>chest X-ray</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cardiomegaly</value>
    </subject>
    <subject>
      <language>eng</language>
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      <language>eng</language>
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      <language>eng</language>
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    <title language="eng">Response to the letter to the editor: On the feasibility of dynamical analysis of network models of biochemical regulation</title>
    <parentTitle language="eng">Bioinformatics</parentTitle>
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    <title language="eng">Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial</title>
    <abstract language="eng">Background&#13;
Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.&#13;
&#13;
Patients and methods&#13;
15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).&#13;
&#13;
Results&#13;
Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.&#13;
&#13;
Conclusions&#13;
We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.</abstract>
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So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.&lt;\/jats:p&gt;\n&lt;\/jats:sec&gt;\n&lt;jats:sec id=\"sec002\"&gt;\n&lt;jats:title&gt;Patients and methods&lt;\/jats:title&gt;\n&lt;jats:p&gt;15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).&lt;\/jats:p&gt;\n&lt;\/jats:sec&gt;\n&lt;jats:sec id=\"sec003\"&gt;\n&lt;jats:title&gt;Results&lt;\/jats:title&gt;\n&lt;jats:p&gt;Using random forests, we developed a predictive machine learning model that discriminated between the situations of \u201cno progress within 100 days before radiological progress\u201d and \u201cprogress within 100 days before radiological progress\u201d. 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    <author>
      <first_name>Thomas</first_name>
      <last_name>Seufferlein</last_name>
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      <last_name>Lausser</last_name>
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    <title language="eng">Permutation-invariant linear classifiers</title>
    <abstract language="eng">Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker.</abstract>
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    <title language="eng">Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation</title>
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    <title language="eng">A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells</title>
    <abstract language="eng">Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.</abstract>
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    <title language="eng">Benchmarking Foundation Models for Mitotic Figure Classification</title>
    <abstract language="eng">The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 % data availability with only 10 % of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance.</abstract>
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    <title language="eng">Is Self-supervision Enough?</title>
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