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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The relationship between physical fitness and academic performance among Chinese college students.
TL;DR: The probability of having poor academic performance was significantly lower among students with high physical fitness than those with low physical fitness; those who belonged to the high overall physical Fitness group had lower odds ratios of low academic performance than those belonging to the low overall physical fitness group.
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The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.
Yu-Hang Zhang,Yu-Hang Zhang,Chen Chu,ShaoPeng Wang,Lei Chen,Jing Lu,Xiangyin Kong,Tao Huang,Haipeng Li,Yu-Dong Cai +9 more
TL;DR: Evidence is shown for a new method in studying drug half-lives and building effective computational methods for the prediction of drugHalf-life.
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The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer.
TL;DR: A weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer, indicates that several chemicals are strongly linked to lung cancer.
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Investigation and Prediction of Human Interactome Based on Quantitative Features.
Xiaoyong Pan,Xiaoyong Pan,Tao Zeng,Yu-Hang Zhang,Lei Chen,Kai-Yan Feng,Tao Huang,Yu-Dong Cai +7 more
TL;DR: A novel computational framework is presented to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo features selection (MCFS), incremental featureselection (IFS), and a quantitative decision-rule system is built to evaluate the potential PPIs under real conditions with random forest and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs.
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Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis.
TL;DR: A method to discriminate between sumoylated lysine residues and acetylated residues was developed and supported the previous finding that there exist different consensus motifs for the two types of PTMs.