Predicting and characterizing a cancer dependency map of tumors with deep learning-Reference-Cited by-同舟云学术

Predicting and characterizing a cancer dependency map of tumors with deep learning

Published:2021-08-20 Issue:34 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Chiu Yu-Chiao¹^ORCID,Zheng Siyuan¹²,Wang Li-Ju¹,Iskra Brian S.¹^ORCID,Rao Manjeet K.¹³^ORCID,Houghton Peter J.¹⁴,Huang Yufei⁵⁶^ORCID,Chen Yidong¹²^ORCID

Affiliation:

1. Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.

2. Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA.

3. Department of Cell Systems and Anatomy, University of Texas Health San Antonio, San Antonio, TX 78229, USA.

4. Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX 78229, USA.

5. University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.

6. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.

Abstract

A novel deep learning method links in vitro CRISPR screens to the context of cancer genomics and translates to individual tumors.

Funder

National Institutes of Health

Cancer Prevention and Research Institute of Texas

Fund for Innovation in Cancer Informatics

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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