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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Identification and analysis of the cleavage site in a signal peptide using SMOTE, dagging, and feature selection methods.
TL;DR: The prediction method proposed in this study was confirmed to be a powerful tool for recognizing cleavage sites from protein sequences, and the optimal features that resulted from the dagging algorithm played crucial roles in identifying the cleaved sites by a literature review.
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Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities.
TL;DR: A novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein–protein interaction network and is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate.
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Identifying protein subcellular locations with embeddings-based node2loc.
TL;DR: In this paper, a network embedding-based method, node2loc, was proposed to identify protein subcellular locations by taking protein-protein interactions (PPIs) into account.
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HIV infection alters the human epigenetic landscape.
TL;DR: Wang et al. as discussed by the authors analyzed the blood DNA methylation data of 485 512 sites in 44 HIV- and 142 HIV-+ patients and applied several advanced computational methods to identify the core distinctive features that were different between the HIV patients and the healthy controls.
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Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods.
TL;DR: Compared to other prediction models that use classic machine learning algorithms as prediction engines on the same datasets with their own optimal features, the optimal ELM-based prediction model produced much better results, indicating the superiority of the proposed model for the identification of nitrated tyrosine residues from protein sequences.