Author

Matan Halfon

Hebrew University of Jerusalem

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Dina Schneidman‐DuhovnyiD· 1 shared Tomer CoheniD· 1 shared

Activity by Year

2021-2021 · peak 1/yr

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2 top collaborators · 1 mutual co-author link

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1 shared paperBioinformatics

Dina Schneidman‐DuhovnyORCID

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1 paper

Recent·Most cited

NanoNet: Rapid end-to-end nanobody modeling by deep learning at sub angstrom resolution

Tomer Cohen, Matan Halfon, Dina Schneidman‐Duhovny

NanoNet is a deep learning model that predicts high-resolution 3D structures of nanobody variable domains from sequences, achieving sub-angstrom accuracy and enabling rapid high-throughput modeling.

bioRxiv · Aug 3, 2021 · PreprintBioinformatics