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.
Papers
More filters
Journal ArticleDOI
Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.
Ning Zhang,Tao Huang,Yu-Dong Cai +2 more
TL;DR: A novel method to predict deleterious non-frameshifting indels based on features extracted from both protein interaction networks and traditional hybrid properties is established, which outperformed existing methods of predicting deleteriously indels and could shed some light on the genetic basis of human genetic variations and human inherited diseases.
Journal ArticleDOI
Identification of transcription factors that may reprogram lung adenocarcinoma
TL;DR: The identification of the transcription factors provides a new insight into its oncogenic role in tumor initiation and progression, and benefits the discovery of functional core set that may reverse malignant transformation and reprogram cancer cells.
Journal ArticleDOI
Identification of Common Genes and Pathways in Eight Fibrosis Diseases.
TL;DR: In this paper, the authors calculated the KEGG and Gene Ontology (GO) enrichment scores of all fibrotic disease genes and compared them with other genes using the Monte Carlo feature selection (MCFS) method.
Journal ArticleDOI
A computational method for the identification of new candidate carcinogenic and non-carcinogenic chemicals.
TL;DR: The analyses identified several candidate carcinogenic chemicals, while those candidates identified as non-carcinogenic were supported by a literature search and exhibit structural dissimilarity with validated carcinogenic/non-cARCinogenic chemicals.
Journal ArticleDOI
Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties
TL;DR: A novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively, and KNN was adopted to build the substrate-enzyme-product interaction network.