Author

Yedid Hoshen

Hebrew University of JerusalemORCID 0000-0002-0967-4541Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Organism:mouse· 1

Frequent Co-Authors

Niv CoheniD· 2 shared Dan OferiD· 1 shared Michal Linial BiD· 1 shared Mor NitzaniD· 1 shared Tomer Michael-Pitschaze· 1 shared Zoe PiraniD· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 9 mutual co-author links

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2 shared papersBioinformatics

Niv CohenORCID

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2 papers

Recent·Most cited

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedJan 5, 2024

Detecting Anomalous Proteins Using Deep Representations

Tomer Michael-Pitschaze, Niv Cohen, Dan Ofer…·Michal Linial B

This paper uses deep learning to detect anomalous proteins by identifying unusual properties in function, phylogenetic families, and sequence segmentation.

9 citations

humanproteomics

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BioinformaticsbioRxivPreprintMar 5, 2023

Biological representation disentanglement of single-cell data

Zoe Piran, Niv Cohen, Yedid Hoshen, Mor Nitzan

This paper introduces biolord, a deep generative framework for disentangling biological factors in single-cell data, enabling prediction of cell states and responses to drug perturbations.

4 citations

mousescRNA-seq

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