H
Haoran Chen
Researcher at Texas A&M University
Publications - 4
Citations - 725
Haoran Chen is an academic researcher from Texas A&M University. The author has contributed to research in topics: Genome & Loss of heterozygosity. The author has an hindex of 3, co-authored 4 publications receiving 411 citations.
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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.
Journal ArticleDOI
Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility
TL;DR: This study established over a CAPRI (Critical Assessment of PRedicted Interactions) target set that the direction of conformational changes from unbound structures and homology models can be reproduced to a great extent by a small set of cNMA modes and developed novel and interpretable features from cN MA and used various machine learning approaches to predict the extent of conformations.
Journal ArticleDOI
Predicting pathogenicity of missense variants with weakly supervised regression.
TL;DR: A novel “weakly supervised” regression (WSR) model is developed that not only predicts precise clinical significance from inexact training annotations but also infers underlying molecular mechanisms in a variant‐specific manner.
Posted ContentDOI
Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression
TL;DR: A novel “weakly supervised” regression model that not only predicts precise clinical significance from inexact training annotations but also infers underlying molecular mechanisms in a variant-specific fashion, corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance.