T
Tao Huang
Researcher at Chinese Academy of Sciences
Publications - 325
Citations - 12593
Tao Huang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 41, co-authored 248 publications receiving 10196 citations. Previous affiliations of Tao Huang include CAS-MPG Partner Institute for Computational Biology & Shanghai Mental Health Center.
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Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties
Tao Huang,Ping Wang,Zhi-Qiang Ye,Heng Xu,Zhisong He,Kai-Yan Feng,Le-Le Hu,Weiren Cui,Kai Wang,Xiao Dong,Lu Xie,Xiangyin Kong,Yu-Dong Cai,Yu-Dong Cai,Yixue Li +14 more
TL;DR: Network features were found to be most important for accurate prediction and can significantly improve the prediction performance, and the results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association.
Journal ArticleDOI
Prediction and analysis of cell‑penetrating peptides using pseudo‑amino acid composition and random forest models
TL;DR: This study determined which features were important for a peptide to be cell-penetrating or non-cell- penetrating and built a predictive model based on the key features extracted from this analysis and an optimal random forest prediction model was built.
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Associations between Ionomic Profile and Metabolic Abnormalities in Human Population
Liang（孙亮） Sun,Yu Yu,Tao Huang,Peng An,Danxia Yu,Zhijie Yu,Huaixing（黎怀星） Li,Hongguang Sheng,Lu Cai,Jun Xue,Miao Jing,Yixue Li,Xu（林旭） Lin,Fudi Wang +13 more
TL;DR: By combining advanced ionomics and mutual information, a quantifying measurement for mutual dependence between two random variables, associations of ion modules/networks with overweight/obesity, metabolic syndrome and type 2 diabetes in 976 middle-aged Chinese men and women are investigated.
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Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles.
TL;DR: The mRMR method and IFS (Incremental Feature Selection) method were used to select a compact feature set for the reduction of feature dimension and improvement of prediction performance and the method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development.