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Tim Beißbarth

As seen in: Nature, Genome Medicine, BioRxiv, medRxiv
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Articles

A comparative survey of functional evidence use in hearing and vision loss genetics

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By Marina T. DiStefano, Tim Beißbarth, Lea M. Starita, Andrew Stergachis, Ahmad Abou Tayoun, Robert B. Hufnagel, Barbara Vona, R. Arda Inan, Sami S. Amr
| Nature Verified
Abstract Despite advances in sequencing and standardized guidelines, many variants, particularly in genetically heterogeneous disorders such as hearing and vision loss, remain of uncertain significance. Multiplexed assays of variant effects (MAVEs) offer a solution to resolve these uncertainties, but their clinical integration is limited. We aimed to assess the utilization of functional evidence in hearing and vision loss genetics.
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DrugOn: A Comprehensive Drug Ontology for Precision Oncology

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By Kevin Kornrumpf, Tim Beißbarth, Vera Gnaß, Myrine Holm
| medRxiv
The authors have declared no competing interest. This study was funded by: Volkswagen Foundation [11-76251-12-1 / 19]; Gemeinsame Bundesausschuss [01NVF20006]; Deutsche Krebshilfe [70113602, 70114018]. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
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Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

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By Hryhorii Chereda, Andreas Leha, Tim Beißbarth
| BioRxiv
New Results Hryhorii Chereda, Andreas Leha, Tim Beißbarth doi: https://doi.org/10.1101/2021.12.26.474194 Abstract Motivation High-throughput technologies play a more and more significant role in discovering prognostic molecular signatures and identifying novel drug targets. It is common to apply Machine Learning (ML) methods to classify high-dimensional gene expression data and to determine a subset of features (genes) that is important for decisions of a ML model.
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