Author

Stefano Rensi

Stanford UniversityORCID 0000-0002-9309-4098Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Russ Biagio AltmaniD· 2 shared Sheng WangiD· 2 shared Dexter R. PrattiD· 1 shared Jian PengiD· 1 shared Jianzhu MaiD· 1 shared Jiawei HaniD· 1 shared Samson H. FongiD· 1 shared Trey E. IdekeriD· 1 shared

Activity by Year

2019-2020 · peak 1/yr

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9 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Russ Biagio AltmanORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 17, 2020

DrugOrchestra: Jointly predicting drug response, targets, and side effects via deep multi-task learning

Yuepeng Jiang, Stefano Rensi, Sheng Wang, Russ Biagio Altman

DrugOrchestra jointly predicts drug response, targets, and side effects using deep multi-task learning, showing improved performance over single-task methods.

9 citations

deep learningdrug response / drug discovery

𝕏 PostBluesky

Systems BiologybioRxivPreprintNov 13, 2019

Deep functional synthesis: a machine learning approach to gene functional enrichment

Sheng Wang, Jianzhu Ma, Samson H. Fong…·Trey E. Ideker

This paper introduces DeepSyn, a machine learning method for gene functional enrichment that uses a knowledge graph to infer gene functions based on disease and drug context, achieving high accuracy in various applications.

8 citations

machine learning (classical)gene regulatory network inference

𝕏 PostBluesky