G
Gregory P. Way
Researcher at Broad Institute
Publications - 58
Citations - 5334
Gregory P. Way is an academic researcher from Broad Institute. The author has contributed to research in topics: Population & Autoencoder. The author has an hindex of 17, co-authored 55 publications receiving 3326 citations. Previous affiliations of Gregory P. Way include University of Colorado Denver & Saint Joseph's University.
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Journal ArticleDOI
Oncogenic Signaling Pathways in The Cancer Genome Atlas
Francisco Sanchez-Vega,Marco Mina,Joshua Armenia,Walid K. Chatila,Augustin Luna,Konnor La,Sofia Dimitriadoy,David L. Liu,Havish S. Kantheti,Sadegh Saghafinia,Debyani Chakravarty,Foysal Daian,Qingsong Gao,Matthew H. Bailey,Wen-Wei Liang,Steven M. Foltz,Ilya Shmulevich,Li Ding,Zachary J. Heins,Angelica Ochoa,Benjamin Gross,Jianjiong Gao,Hongxin Zhang,Ritika Kundra,Cyriac Kandoth,Istemi Bahceci,Leonard Dervishi,Ugur Dogrusoz,Wanding Zhou,Hui Shen,Peter W. Laird,Gregory P. Way,Casey S. Greene,Han Liang,Yonghong Xiao,Chen Wang,Antonio Iavarone,Alice H. Berger,Trever G. Bivona,Alexander J. Lazar,Gary D. Hammer,Thomas J. Giordano,Lawrence N. Kwong,Grant A. McArthur,Chenfei Huang,Aaron D. Tward,Mitchell J. Frederick,Frank McCormick,Matthew Meyerson,Eliezer M. Van Allen,Andrew D. Cherniack,Giovanni Ciriello,Chris Sander,Nikolaus Schultz +53 more
TL;DR: This work charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity.
Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Journal ArticleDOI
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
Theo A. Knijnenburg,Linghua Wang,Michael T. Zimmermann,Nyasha Chambwe,Galen F. Gao,Andrew D. Cherniack,Huihui Fan,Hui Shen,Gregory P. Way,Casey S. Greene,Yuexin Liu,Rehan Akbani,Bin Feng,Lawrence A. Donehower,Chase Miller,Yang Shen,Mostafa Karimi,Haoran Chen,Pora Kim,Peilin Jia,Eve Shinbrot,Shaojun Zhang,Jianfang Liu,Hai Hu,Matthew H. Bailey,Christina Yau,Denise M. Wolf,Zhongming Zhao,John N. Weinstein,Lei Li,Li Ding,Gordon B. Mills,Peter W. Laird,David A. Wheeler,Ilya Shmulevich,Raymond J. Monnat,Yonghong Xiao,Chen Wang +37 more
TL;DR: These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy and a new machine-learning-based classifier developed from gene expression data allowed to identify alterations that phenocopy deleterious TP53 mutations.
Proceedings ArticleDOI
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
Gregory P. Way,Casey S. Greene +1 more
TL;DR: The extent to which a Variational autoencoders can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features are determined.
Journal ArticleDOI
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Gregory P. Way,Francisco Sanchez-Vega,Konnor La,Joshua Armenia,Walid K. Chatila,Augustin Luna,Chris Sander,Andrew D. Cherniack,Marco Mina,Giovanni Ciriello,Nikolaus Schultz,Yolanda Sanchez,Casey S. Greene +12 more
TL;DR: A machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification and data that suggest that multiple hits in the Ras pathway confer increased Ras activity are presented.