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Zixing Wang

Researcher at University of Texas MD Anderson Cancer Center

Publications -  32
Citations -  3206

Zixing Wang is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Cancer & Programmed cell death. The author has an hindex of 16, co-authored 31 publications receiving 2309 citations. Previous affiliations of Zixing Wang include University of Tennessee & University of Texas Health Science Center at Houston.

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Comprehensive Characterization of Cancer Driver Genes and Mutations.

Matthew H. Bailey, +45 more
- 05 Apr 2018 - 
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.
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Erratum: Comprehensive Characterization of Cancer Driver Genes and Mutations (ARTICLE (2018) 173(2) (371–385), (S009286741830237X), (10.1016/j.cell.2018.02.060))

Matthew A. Bailey, +761 more
- 09 Aug 2018 - 
TL;DR: In this article, the authors made two errors in preparation of this manuscript and corrected them in the revised Figure S7 by re-ordering the mutations by frequency for COAD and READ independently.
Journal Article

Comprehensive Characterization of Cancer Driver Genes and Mutations (vol 173, 371.e1, 2018)

Matthew A. Bailey, +44 more
- 01 Jan 2018 - 
TL;DR: The MC3 Working Group, The Cancer Genome Atlas Research Network, Michael S. Lawrence, Adam Godzik, Nuria Lopez-Bigas, Josh Stuart, David Wheeler, Gad Getz, Ken Chen, Alexander J. Lazar, Gordon B. Mills, Rachel Karchin, Li Ding are the authors of this paper.
Journal ArticleDOI

Systematic Functional Annotation of Somatic Mutations in Cancer.

Patrick Kwok Shing Ng, +42 more
- 12 Mar 2018 - 
TL;DR: A moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations is reported.
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Identifying microRNA targets in different gene regions

Wenlong Xu, +5 more
- 28 May 2014 - 
TL;DR: A simple but powerful computational miRNA target prediction method is developed that is solely based on canonical and non-canonical seed matches without requiring evolutionary conservation of the target sites, which improves the sensitivity of mi RNA target identification, while achieving a comparable accuracy with existing methods.