M
Michael Snyder
Researcher at Stanford University
Publications - 938
Citations - 150929
Michael Snyder is an academic researcher from Stanford University. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 169, co-authored 840 publications receiving 130225 citations. Previous affiliations of Michael Snyder include Wyss Institute for Biologically Inspired Engineering & Public Health Research Institute.
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Journal ArticleDOI
Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements
Josh Tycko,Michael Wainberg,Georgi K. Marinov,Oana Ursu,Gaelen T. Hess,Braeden K. Ego,Aradhana,Amy Li,Alisa Truong,Alexandro E. Trevino,Kaitlyn Spees,David Yao,Irene M. Kaplow,Peyton Greenside,David W. Morgens,Douglas H. Phanstiel,Douglas H. Phanstiel,Michael Snyder,Lacramioara Bintu,William J. Greenleaf,Anshul Kundaje,Michael C. Bassik +21 more
TL;DR: The authors find Cas9 nuclease, CRISPRi/a each have distinct off-target effects, and that these can be accurately identified and removed using the GuideScan sgRNA specificity score.
Journal ArticleDOI
Omics Profiling in Precision Oncology.
Kun-Hsing Yu,Michael Snyder +1 more
TL;DR: The techniques used for tumor omics analysis are summarized, the key findings in cancer omics studies are recapitulated, and areas requiring further research on precision oncology are pointed to.
Journal ArticleDOI
Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations
Carlos L. Araya,Can Cenik,Jason A. Reuter,Gert Kiss,Vijay S. Pande,Michael Snyder,William J. Greenleaf +6 more
TL;DR: D density-based clustering methods in 21 tumor types are employed to detect variably sized significantly mutated regions (SMRs), revealing recurrent alterations across a spectrum of coding and noncoding elements, including transcription factor binding sites and untranslated regions mutated in up to ∼15% of specific tumor types.
Journal ArticleDOI
An integrative ENCODE resource for cancer genomics
Jing Zhang,Dong-Hoon Lee,Vineet K. Dhiman,Peng Jiang,Peng Jiang,Jie Xu,Jie Xu,Patrick McGillivray,Hongbo Yang,Jason Liu,William Meyerson,Declan Clarke,Mengting Gu,Shantao Li,Shaoke Lou,Jinrui Xu,Lucas Lochovsky,Matthew Ung,Lijia Ma,Lijia Ma,Shan Yu,Qin Cao,Arif Harmanci,Koon-Kiu Yan,Anurag Sethi,Gamze Gürsoy,Michael Rutenberg Schoenberg,Joel Rozowsky,Jonathan Warrell,Prashant Emani,Yucheng T. Yang,Timur R. Galeev,Xiangmeng Kong,Shuang Liu,Xiaotong Li,Jayanth Krishnan,Yanlin Feng,Juan Carlos Rivera-Mulia,Juan Carlos Rivera-Mulia,Jessica Adrian,James R. Broach,Michael J. Bolt,Jennifer R. Moran,Dominic Fitzgerald,Vishnu Dileep,Tingting Liu,Shenglin Mei,Takayo Sasaki,Claudia Trevilla-Garcia,Claudia Trevilla-Garcia,Su Wang,Yanli Wang,Chongzhi Zang,Daifeng Wang,Robert J. Klein,Michael Snyder,David M. Gilbert,Kevin Y. Yip,Chao Cheng,Chao Cheng,Feng Yue,Feng Yue,X. Shirley Liu,Kevin P. White,Mark Gerstein +64 more
TL;DR: A custom annotation within ENCODE for cancer is presented, highlighting a workflow that can help prioritise key elements in oncogenesis and targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the E NCODE resource.
Journal ArticleDOI
Wearable sensors enable personalized predictions of clinical laboratory measurements.
Jessilyn Dunn,Lukasz Kidzinski,Ryan Runge,Daniel M. Witt,Jennifer L. Hicks,Sophia Miryam Schüssler-Fiorenza Rose,Sophia Miryam Schüssler-Fiorenza Rose,Xiao Li,Xiao Li,Amir Bahmani,Scott L. Delp,Trevor Hastie,Michael Snyder,Michael Snyder +13 more
TL;DR: In this article, the authors examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models.