Gherman Novakovsky

Biologically relevant transfer learning improves transcription factor binding prediction

S. Mostafavi, W. Wasserman, O. Fornes, 2020, Genome Biology.

Petabase-scale sequence alignment catalyses viral discovery

Robert C. Edgar, J. Banfield, R. Edgar, 2020, Nature.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

C. Gaiteri, D. Bennett, S. Mostafavi, 2019, American journal of human genetics.

ExplaiNN: interpretable and transparent neural networks for genomics

S. Mostafavi, W. Wasserman, O. Fornes, 2022, bioRxiv.

ExplaiNN: interpretable and transparent neural networks for genomics

S. Mostafavi, W. Wasserman, O. Fornes, 2022, bioRxiv.

Obtaining genetics insights from deep learning via explainable artificial intelligence

Maxwell W. Libbrecht, W. Wasserman, Gherman Novakovsky, 2022, Nature Reviews Genetics.

Biologically relevant transfer learning improves transcription factor binding prediction

S. Mostafavi, W. Wasserman, O. Fornes, 2021, Genome biology.

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm

S. Mostafavi, P. Pavlidis, W. Wasserman, 2021, bioRxiv.

A multimorphic mutation in IRF4 causes human autosomal dominant combined immunodeficiency

U. Pannicke, K. Schwarz, J. Casanova, 2023, Science Immunology.

Tonic-signaling chimeric antigen receptors drive human regulatory T cell exhaustion

C. Mackall, M. Levings, Gherman Novakovsky, 2023, Proceedings of the National Academy of Sciences of the United States of America.