Author

Taiasean Wu

University of California, San FranciscoORCID 0000-0001-6988-1414Unclaimed 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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Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Approach:statistical model· 2 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Research Focus:software / tool· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Douglas R. WassarmaniD· 2 shared Alexander M. WolffiD· 1 shared Alison M. MaxwelliD· 1 shared Andrew M. NataleiD· 1 shared Beatrice E. AryiD· 1 shared Bruk MensaiD· 1 shared Charlotte NelsoniD· 1 shared Cole V. M. HelselliD· 1 shared

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2018-2022 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Douglas R. WassarmanORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 2, 2022

Cellpanelr: identify predictive biomarkers from cell line panel response data

Douglas R. Wassarman, Taiasean Wu, Kevan M. Shokat

Cellpanelr identifies mutation and expression biomarkers from cell line panel response data using DepMap datasets.

1 citation

statistical modelmachine learning (classical)

𝕏 PostBluesky

BioinformaticsBiology OpenPeer-reviewedJul 15, 2018

Extending Chemical Perturbations Of The Ubiquitin Fitness Landscape In A Classroom Setting

David Mavor, Kyle A. Barlow, Daniel E. Asarnow…·James S. Fraser

The paper uses deep mutational scanning to explore how chemical perturbations affect ubiquitin's mutational tolerance, revealing sensitization of most positions except two.

25 citations

proteomicsdeep learning

𝕏 PostBluesky