Author

Zhengtao Chu

Huntsman Cancer InstituteORCID 0000-0002-7072-064XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Assay:bulk RNA-seq· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

Alana L. WelmiD· 2 shared Aaron E. AtkinsoniD· 1 shared Apurva Srivastava· 1 shared Bryan E. WelmiD· 1 shared Bryan Welm· 1 shared Casey SedermaniD· 1 shared Chieh-Hsiang Yang· 1 shared Chieh‐Hsiang Yang· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersCancer Biology

Alana L. WelmORCID

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2 papers

Recent·Most cited

Response to SMAC mimetics in patient-derived models of triple-negative breast cancer relies on RIPK1 activation

Elisabeth Brown, Paige Jensen, Chieh-Hsiang Yang, Emilio Cortes Sanchez, Zhengtao Chu, Hui Yang, Yi Qiao, 義行高橋, Apurva Srivastava, Bryan Welm, Jaime Fornetti, Alana L. Welm

This study identifies RIPK1 activation as a key determinant of response to SMAC mimetics in TNBC, showing that resistance is linked to impaired RIPK1 autophosphorylation and HOIP overexpression.

bioRxiv · Oct 28, 2026 · PreprintCancer Biology

A precision oncology-focused deep learning framework for personalized selection of cancer therapy

Casey Sederman, Chieh‐Hsiang Yang, Emilio Cortes-Sanchez, Tonya DiSera, Xiaomeng Huang, Sandra D. Scherer, Ling Zhao, Zhengtao Chu, Eliza R. White, Aaron E. Atkinson, Jadon Wagstaff, Katherine Elena Varley, Michael T. Lewis, Yi Qiao, Bryan E. Welm, , Gábor Marth

This paper introduces ScreenDL, a deep learning model that predicts effective cancer therapies using tumor omic and drug screening data, outperforming existing methods in preclinical trials.

bioRxiv · Dec 12, 2024 · PreprintCancer Biology