Burkhard Rost

Affiliations: 
Technische Universität München (TUM), München, Bayern, Germany 
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"Burkhard Rost"

Parents

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Heinz Horner research assistant 1986-1988 Heidelberg University
 (Topic for Master Thesis: Learning algorithms for spin-glass-like neural networks')
Chris Sander grad student 1987-1992 EMBL Heidelberg

Children

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Yanay Ofran grad student 2004 Columbia
Dariusz S. Przybylski grad student 2005 Columbia
Avner Schlessinger grad student 2007 Columbia
Yana Bromberg grad student 2002-2007 Columbia
Ta-Tsen Soong grad student 2009 Columbia
Kazimierz O. Wrzeszczynski grad student 2009 Columbia
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Publications

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Erckert K, Birkeneder F, Rost B. (2025) bindNode24: Competitive binding residue prediction with 60 % smaller model. Computational and Structural Biotechnology Journal. 27: 1060-1066
Senoner T, Olenyi T, Heinzinger M, et al. (2025) ProtSpace: A Tool for Visualizing Protein Space. Journal of Molecular Biology. 168940
Olenyi T, Marquet C, Grekova A, et al. (2025) TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins. Journal of Molecular Biology. 168997
Heinzinger M, Rost B. (2025) Teaching AI to speak protein. Current Opinion in Structural Biology. 91: 102986
Weissenow K, Rost B. (2025) Are protein language models the new universal key? Current Opinion in Structural Biology. 91: 102997
Heinzinger M, Weissenow K, Sanchez JG, et al. (2024) Bilingual language model for protein sequence and structure. Nar Genomics and Bioinformatics. 6: lqae150
Marquet C, Schlensok J, Abakarova M, et al. (2024) Expert-guided protein language models enable accurate and blazingly fast fitness prediction. Bioinformatics (Oxford, England). 40
Zatorski N, Sun Y, Elmas A, et al. (2024) Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer. Iscience. 27: 110640
Erckert K, Rost B. (2024) Assessing the role of evolutionary information for enhancing protein language model embeddings. Scientific Reports. 14: 20692
Llorián-Salvador Ó, Windeler N, Martin N, et al. (2024) CT-based radiomics for predicting breast cancer radiotherapy side effects. Scientific Reports. 14: 20051
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