Author

Thomas Villmann

Hochschule MittweidaORCID 0000-0001-6725-0141Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:protein structure prediction· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1 Assay:protein sequence· 1 Assay:protein structure· 1

Frequent Co-Authors

Marika KadeniD· 3 shared Christoph LeberechtiD· 2 shared Katrin Sophie BohnsackiD· 2 shared Mirko WeberiD· 2 shared Dirk LabuddeiD· 1 shared Florian KaiseriD· 1 shared Jacek BłażewicziD· 1 shared Julia AbeliD· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Marika KadenORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintSep 3, 2020

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

Julia Abel, Marika Kaden, Katrin Sophie Bohnsack…·Thomas Villmann

The paper uses interpretable machine learning to classify protein structures as native or mirror based on Ramachandran plot analysis with high accuracy.

1 citation

protein structuremachine learning (classical)

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMay 15, 2020

Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models

Marika Kaden, Katrin Sophie Bohnsack, Mirko Weber…·Thomas Villmann

The paper applies interpretable machine learning to classify and analyze SARS-CoV-2 sequences, identifying potential new virus types through rejected classifications.

3 citations

virusbulk RNA-seq

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BioinformaticsBioData MiningPeer-reviewedJan 5, 2019

Application of an Interpretable Classification Model on Early Folding Residues during Protein Folding

Sebastian Bittrich, Marika Kaden, Christoph Leberecht…·Dirk Labudde

The paper applies GMLVQ to predict early folding residues in proteins, achieving 76.6% AUC and highlighting the importance of secondary structure and hydrophobic networks.

30 citations

protein sequencestatistical model

𝕏 PostBluesky