@article{KustermannMantaPaoneetal.2018, author = {Kustermann, Monika and Manta, Linda and Paone, Christoph and Kustermann, Jochen and Lausser, Ludwig and Wiesner, Cora and Eichinger, Ludwig and Clemen, Christoph S. and Schr{\"o}der, Rolf and Kestler, Hans A. and Sandri, Marco and Rottbauer, Wolfgang and Just, Steffen}, title = {Loss of the novel Vcp (valosin containing protein) interactor Washc4 interferes with autophagy-mediated proteostasis in striated muscle and leads to myopathy in vivo}, volume = {14 (2018)}, journal = {Autophagy}, number = {11}, publisher = {Taylor \& Francis}, address = {London}, issn = {1554-8627}, doi = {https://doi.org/10.1080/15548627.2018.1491491}, pages = {1911 -- 1927}, year = {2018}, abstract = {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.}, language = {en} } @article{MuellerLausserWilhelmetal.2020, author = {M{\"u}ller, Andr{\´e} and Lausser, Ludwig and Wilhelm, Adalbert and Ropinski, Timo and Platzer, Matthias and Neumann, Heiko and Kestler, Hans A.}, title = {A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data}, volume = {15}, journal = {Advances in Data Analysis and Classification}, number = {2}, publisher = {Springer}, address = {Berlin}, issn = {1862-5355}, doi = {https://doi.org/10.1007/s11634-020-00416-5}, pages = {463 -- 480}, year = {2020}, abstract = {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.}, language = {en} } @article{BuchholzLausserSchenketal.2024, author = {Buchholz, Malte and Lausser, Ludwig and Schenk, Miriam and Earl, Julie and Lawlor, Rita T. and Scarpa, Aldo and Sanjuanbenito, Alfonso and Carrato, Alfredo and Malats, Nuria and Tjaden, Christine and Giese, Nathalia A. and B{\"u}chler, Markus and Hackert, Thilo and Kestler, Hans A. and Gress, Thomas M.}, title = {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}, volume = {13}, journal = {United European Gastroenterology Journal}, number = {3}, publisher = {Wiley}, address = {Hoboken}, issn = {2050-6414}, doi = {https://doi.org/10.1002/ueg2.12676}, pages = {353 -- 363}, year = {2024}, abstract = {Background and Aims 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. Methods 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. Results 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. Conclusions 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.}, language = {en} } @article{LausserKubisRottbaueretal.2018, author = {Lausser, Ludwig and Kubis, Lea and Rottbauer, Wolfgang and Frank, Derk and Just, Steffen and Kestler, Hans A.}, title = {Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species}, volume = {8 (2018)}, pages = {158}, journal = {Biomolecules}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {2218-273X}, doi = {https://doi.org/10.3390/biom8040158}, year = {2018}, abstract = {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.}, language = {en} } @article{HuehneKesslerFuerstbergeretal.2018, author = {H{\"u}hne, Rolf and Kessler, Viktor and F{\"u}rstberger, Axel and Werle, Silke D. and Platzer, Matthias and S{\"u}hnel, J{\"u}rgen and Lausser, Ludwig and Kestler, Hans A.}, title = {3D Network exploration and visualisation for lifespan data}, volume = {19 (2018)}, pages = {390}, journal = {BMC Bioinformatics}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {https://doi.org/10.1186/s12859-018-2393-x}, year = {2018}, abstract = {Background 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. Results 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. Conclusion 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.}, language = {en} } @article{LausserSchaeferKestler2018, author = {Lausser, Ludwig and Sch{\"a}fer, Lisa M. and Kestler, Hans A.}, title = {Ordinal Classifiers Can Fail on Repetitive Class Structures}, volume = {4 (2018)}, journal = {Archives of Data Science, Series A}, number = {1}, publisher = {KIT Scientific Publishing}, address = {Karlsruhe}, issn = {2363-9881}, doi = {https://doi.org/10.5445/KSP/1000085951/25}, year = {2018}, abstract = {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.}, language = {en} } @article{LausserSchaeferSchirraetal.2019, author = {Lausser, Ludwig and Sch{\"a}fer, Lisa M. and Schirra, Lyn-Rouven and Szekely, Robin and Schmid, Florian and Kestler, Hans A.}, title = {Assessing phenotype order in molecular data}, volume = {9}, pages = {11746}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-019-48150-z}, year = {2019}, abstract = {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.}, language = {en} } @article{LausserSzekelyKlimmeketal.2020, author = {Lausser, Ludwig and Szekely, Robin and Klimmek, Attila and Schmid, Florian and Kestler, Hans A.}, title = {Constraining classifiers in molecular analysis}, volume = {17 (2020)}, pages = {20190612}, journal = {Journal of the Royal Society Interface}, subtitle = {invariance and robustness}, number = {163}, publisher = {The Royal Society}, address = {London}, issn = {1742-5662}, doi = {https://doi.org/10.1098/rsif.2019.0612}, year = {2020}, abstract = {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.}, language = {en} } @article{BellmannLausserKestleretal.2022, author = {Bellmann, Peter and Lausser, Ludwig and Kestler, Hans A. and Schwenker, Friedhelm}, title = {A Theoretical Approach to Ordinal Classification}, volume = {12 (2022)}, pages = {1815}, journal = {Applied Sciences}, subtitle = {Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures}, number = {4}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {https://doi.org/10.3390/app12041815}, year = {2022}, abstract = {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.}, language = {en} } @article{WeidnerSchwabWerleetal.2021, author = {Weidner, Felix M. and Schwab, Julian D. and Werle, Silke D. and Ikonomi, Nensi and Lausser, Ludwig and Kestler, Hans A.}, title = {Capturing dynamic relevance in Boolean networks using graph theoretical measures}, volume = {37}, journal = {Bioinformatics}, number = {20}, publisher = {Oxford University Press}, address = {Oxford}, issn = {1460-2059}, doi = {https://doi.org/10.1093/bioinformatics/btab277}, pages = {3530 -- 3537}, year = {2021}, abstract = {Motivation 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. Results 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.}, language = {en} }