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

Co-Author Collaboration Network

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

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.

bioRxiv · Nov 17, 2020 · PreprintBioinformatics

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

Sheng Wang, Jianzhu Ma, Samson H. Fong, Stefano Rensi, Jiawei Han, Jian Peng, Dexter R. Pratt, Russ Biagio Altman, 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.

bioRxiv · Nov 13, 2019 · PreprintSystems Biology