Author

Tanya Hochner

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· 2

Signature Assays, Organisms & Methods

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Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:protein sequence· 1 Research Focus:protein structure prediction· 1 Assay:proteomics· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Dina Schneidman‐DuhovnyiD· 2 shared Alon Aronson· 1 shared Barak RavehiD· 1 shared Dor Stein· 1 shared Ora Schueler‐FurmaniD· 1 shared Tomer CoheniD· 1 shared Yuh Min ChookiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Dina Schneidman‐DuhovnyORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 24, 2026

CliPepPI: Scalable prediction of domain-peptide specificityusing contrastive learning

Tanya Hochner, Dor Stein, Ora Schueler‐Furman…·Dina Schneidman‐Duhovny

CLIPepPI predicts domain-peptide specificity using contrastive learning, achieving competitive performance across benchmarks and enabling proteome-wide analysis.

DOI 10.64898/2026.03.18.712595

protein sequencecontrastive learning

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BioinformaticsbioRxivPreprintDec 15, 2022

Structure modeling and specificity of peptide-MHC class I interactions using geometric deep learning

Alon Aronson, Tanya Hochner, Tomer Cohen, Dina Schneidman‐Duhovny

The paper introduces MHCfold, a deep learning model that predicts peptide-MHC class I structures and binding specificity, achieving high accuracy and enabling high-throughput analysis.

10 citations

humanproteomics

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