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  <doc>
    <id>8451</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1470</pageFirst>
    <pageLast>1472</pageLast>
    <pageNumber/>
    <edition>38</edition>
    <issue>5</issue>
    <volume/>
    <type>article</type>
    <publisherName>Oxford Academic</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides</title>
    <abstract language="eng">We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs, and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD, and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling, notably optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we perform a reanalysis of four public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to more than 10% of the total number of peptides identified (43,501 out of 402,512).</abstract>
    <parentTitle language="eng">Bioinformatics</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Husen M. Umer</author>
    <submitter>Julianus Pfeuffer</submitter>
    <author>Yafeng Zhu</author>
    <author>Julianus Pfeuffer</author>
    <author>Timo Sachsenberg</author>
    <author>Janne Lehtiö</author>
    <author>Rui Branca</author>
    <author>Yasset Perez-Riverol</author>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>8429</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>10</issue>
    <volume>16</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-10-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative</title>
    <abstract language="eng">Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA.&#13;
Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated.&#13;
Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively.&#13;
Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN- based approaches. To foster such developments we make all segmentations publicly available together with this publication.</abstract>
    <parentTitle language="eng">PLOS One</parentTitle>
    <identifier type="doi">10.1371/journal.pone.0258855</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">07.10.2021</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Alexander Tack</author>
    <submitter>Alexander Tack</submitter>
    <author>Felix Ambellan</author>
    <author>Stefan Zachow</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="BMBF-TOKMIS">BMBF-TOKMIS</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="persons" number="ambellan">Ambellan, Felix</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="projects" number="DFG_KneeKinematics">DFG_KneeKinematics</collection>
    <collection role="projects" number="MathPlus - TrU-2">MathPlus - TrU-2</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>8373</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>127</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2021-08-11</thesisDateAccepted>
    <title language="eng">Investigation of Options to Handle 3D MRI Data via Convolutional Neural Networks Application in Knee Osteoarthritits Classification</title>
    <enrichment key="opus.source">publish</enrichment>
    <advisor>Alexander Tack</advisor>
    <author>Jan Krause</author>
    <submitter>Stefan Zachow</submitter>
    <referee>Stefan Zachow</referee>
    <advisor>Stefan Zachow</advisor>
    <referee>Sebastian Sager</referee>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computational Diagnosis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knee Osteoarthritis</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="68-XX">COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <thesisGrantor>Otto-von-Guericke-Universität Magdeburg</thesisGrantor>
  </doc>
  <doc>
    <id>8376</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>96</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2021-09-20</thesisDateAccepted>
    <title language="eng">A Deep Learning Method for Automated Detection of Meniscal Tears in Meniscal Sub-Regions in 3D MRI Data</title>
    <abstract language="eng">This work presents a fully automated pipeline, centered around a deep neural network, as well as a method to train that network in an efficient manner, that enables accurate detection of lesions in meniscal anatomical subregions. The network architecture is based on a transformer encoder/decoder. It is trained on DESS and tuned on IW TSE 3D MRI scans sourced from the Osteoarthritis Initiative. Furthermore, it is trained in a multilabel, and multitask fashion, using an auxiliary detection head. The former enables implicit localisation&#13;
of meniscal defects, that to the best of my knowledge, has not yet been reported elsewhere. The latter enables efficient learning on the entire 3D MRI volume. Thus, the proposed method does not require any expert knowledge at inference. Aggregated inference results from two datasets resulted in an overall AUCROC result of 0.90, 0.91 and 0.93 for meniscal lesion detection anywhere in the knee, in medial and in lateral menisci respectively. These results compare very well to the related work, even though only a fraction of the data has been utilized. Clinical applicability and benefit is yet to be determined.</abstract>
    <enrichment key="opus.source">publish</enrichment>
    <advisor>Tack Alexander</advisor>
    <author>Alexey Shestakov</author>
    <submitter>Stefan Zachow</submitter>
    <referee>Stefan Zachow</referee>
    <advisor>Zachow Stefan</advisor>
    <referee>Tim Conrad</referee>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Computational Diagnosis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Knee Osteoarthritis</value>
    </subject>
    <collection role="ccs" number="J.">Computer Applications</collection>
    <collection role="msc" number="68-XX">COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)</collection>
    <collection role="persons" number="zachow">Zachow, Stefan</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="Overload-PrevOP-SP4">Overload-PrevOP-SP4</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <thesisGrantor>Freie Universität Berlin</thesisGrantor>
  </doc>
  <doc>
    <id>9192</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>26</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Explicit modeling of antibody levels for infectious disease simulations in the context of SARS-CoV-2</title>
    <abstract language="eng">Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These antibody levels are dynamic: due to waning, antibody levels will drop over time. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to support the planning of public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. The model was developed in response to the complexity of different immunization sequences and types and is based on neutralization titer studies. This approach allows complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.</abstract>
    <parentTitle language="eng">iScience</parentTitle>
    <identifier type="doi">10.1016/j.isci.2023.107554</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">07.08.2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Sebastian Müller</author>
    <submitter>Tim Conrad</submitter>
    <author>Sydney Paltra</author>
    <author>Jakob Rehmann</author>
    <author>Kai Nagel</author>
    <author>Tim Conrad</author>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="MODUS-COVID">MODUS-COVID</collection>
  </doc>
  <doc>
    <id>9298</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>50</volume>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-10-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks</title>
    <abstract language="eng">Aims: Interpretation of continuous EEG is a demanding task that requires the expertise of trained neurologists. However, these experts are not always available in many medical centers. As part of the 2023 George B. Moody PhysioNet Challenge, we developed a deep learning based method for analyzing EEG data of comatose patients and predicting prognosis following cardiac arrest. Methods: Our approach is a two-step pipeline that consists of a prediction model and a decision-making strategy. The prediction model is a residual neural network (ResNet-18) that extracts features and makes a prediction based on a short 5-minute EEG recording. In the second step, a majority vote over multiple predictions made for several EEG recordings of a patient determines the final prognosis. Results: Based on 10-fold cross-validation on the training set, we achieved a true positive rate (TPR) of 0.41 for predicting poor outcome while keeping the false positive rate below 0.05 at 72 hours after recovery of spontaneous circulation. On the official challenge leaderboard, our team ZIB_Visual scored 0.426 TPR. Conclusion: Our approach, while simple to implement and execute, faced overfitting challenges during the official competition phase. In this paper, we discuss our implementation and potential improvements to address these issues.</abstract>
    <parentTitle language="eng">Computing in Cardiology (CinC) 2023</parentTitle>
    <identifier type="doi">10.22489/CinC.2023.093</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <submitter>Tim Conrad</submitter>
    <author>Kuba Weimann</author>
    <author>Tim Conrad</author>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="kuba.weimann">Weimann, Kuba</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>9247</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-10-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification</title>
    <abstract language="eng">Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals (denote HCPs from here on) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to alert for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in a relatively high false-positive rate) and this, combined with the device's nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing amount of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics that make its analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. It does so by combining high-frequency noise detection (which often occurs in ICM data) with a semi-supervised learning pipeline that allows for re-labeling of training episodes, and by using segmentation and dimension reduction techniques that are robust to morphology variations of the sECG signal (which are typical to ICM data). As a result, it performs better than state-of-the-art techniques on such data with e.g. F1 score of 0.51 vs. 0.38 of our baseline state-of-the-art technique in correctly calling Atrial Fibrilation in ICM data. As such, it could be used in numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs by, e.g., suggesting a rhythm type.</abstract>
    <parentTitle language="eng">Machine Learning and Knowledge Extraction</parentTitle>
    <identifier type="doi">10.3390/make5040077</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2023-10-18</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Amnon Bleich</author>
    <submitter>Tim Conrad</submitter>
    <author>Antje Linnemann</author>
    <author>Benjamin Jaidi</author>
    <author>Bjoern H Diem</author>
    <author>Tim Conrad</author>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>9251</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1665</pageFirst>
    <pageLast>1885</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>124</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-10-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates</title>
    <abstract language="eng">The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre-trained deep learning-based protein embeddings and machine learning classification approaches and outperforms other state-of-the-art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM-1b) and their performances for this specific task.</abstract>
    <parentTitle language="eng">Journal of Cellular Biochemistry</parentTitle>
    <identifier type="doi">10.1002/jcb.30490</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">epub ahead of print</enrichment>
    <enrichment key="AcceptedDate">2023-10-23</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Marco Anteghini</author>
    <submitter>Tim Conrad</submitter>
    <author>Vitor AP Martins dos Santos</author>
    <author>Edoardo Saccenti</author>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>9223</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>405</pageFirst>
    <pageLast>411</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2643</volume>
    <type>bookpart</type>
    <publisherName>Humana, New York</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Computational Approaches for Peroxisomal Protein Localization</title>
    <abstract language="eng">Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.</abstract>
    <parentTitle language="deu">Peroxisomes</parentTitle>
    <identifier type="isbn">978-1-0716-3047-1</identifier>
    <identifier type="doi">10.1007/978-1-0716-3048-8_29</identifier>
    <enrichment key="Series">Methods in Molecular Biology</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2023-09-01</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Marco Anteghini</author>
    <submitter>Tim Conrad</submitter>
    <author>Vitor Martins Dos Santos</author>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>9237</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Bravo MaRDI: A Wikibase Knowledge Graph on Mathematics</title>
    <abstract language="deu">Mathematical world knowledge is a fundamental component of Wikidata. However, to date, no expertly curated knowledge graph has focused specifically on contemporary mathematics. Addressing this gap, the Mathematical Research Data Initiative (MaRDI) has developed a comprehensive knowledge graph that links multimodal research data in mathematics. This encompasses traditional research data items like datasets, software, and publications and includes semantically advanced objects such as mathematical formulae and hypotheses. This paper details the abilities of the MaRDI knowledge graph, which is based on Wikibase, leading up to its inaugural public release, codenamed Bravo, available on https://portal.mardi4nfdi.de.</abstract>
    <parentTitle language="deu">Proceedings of the 4th Wikidata Workshop 2022 co-located with the 22st International Semantic Web Conference (ISWC2023)</parentTitle>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="SubmissionStatus">accepted for publication</enrichment>
    <enrichment key="AcceptedDate">2023-09-01</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Moritz Schubotz</author>
    <submitter>Tim Conrad</submitter>
    <author>Eloi Ferrer</author>
    <author>Johannes Stegmüller</author>
    <author>Daniel Mietchen</author>
    <author>Olaf Teschke</author>
    <author>Larissa Pusch</author>
    <author>Tim Conrad</author>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
