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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Analysis of the relationship between PM2.5 and lung cancer based on protein-protein interactions.
TL;DR: Analysis of small/nonsmall cell lung cancer genes with high scores revealed that it is theoretically possible that PM2.5 is an etiologic factor for lung cancer, and provided new insights of the relationship between lung cancers and air pollution.
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Integrative Analysis Reveals Enhanced Regulatory Effects of Human Long Intergenic Non-Coding RNAs in Lung Adenocarcinoma
You Zhou,Kai Wu,Jianping Jiang,Jinfei Huang,Peiwei Zhang,Yufei Zhu,Guohong Hu,Jingyu Lang,Yufang Shi,Landian Hu,Tao Huang,Xiangyin Kong +11 more
TL;DR: A comprehensive landscape of RNA-seq transcriptome profiles of lung adenocarcinomas and their paired normal counterparts is presented to unravel gene regulation rules of lincRNAs and reveals enhanced regulatory effects of l incRNAs.
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Functional association between influenza A (H1N1) virus and human.
TL;DR: A computational framework to investigate the crosstalk between the virus and the host, by finding out the proteins that the virus is attacking, provides some knowledge of the possible biological processes and molecular interactions caused by the viral infection, including the host responses.
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Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach
TL;DR: This study proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network and outperformed the nearest neighbor algorithm adopting pseudo amino acid composition.
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Predicting A-to-I RNA editing by feature selection and random forest.
TL;DR: A novel method to predict RNA editing based on a random forest method based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms, with results suggesting that a small feature set was sufficient to achieve accurate prediction.