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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A computational method for the identification of candidate drugs for non-small cell lung cancer.
TL;DR: A computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer, using a powerful clustering algorithm, the EM algorithm is proposed.
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Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods
TL;DR: This study contributes to revealing potential expression features and provides a reference for patient stratification by identifying key blood methylation features and rules that can distinguish the severity of SARS-CoV-2 infection.
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Changes in working memory performance and cortical activity during acute aerobic exercise in young adults
TL;DR: In this article , the concurrent performance of working memory and cortical activity during acute aerobic exercise in young adults was examined by using functional near-infrared spectroscopy (fNIRS) to measure cortex activation.
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Identifying Infliximab- (IFX-) Responsive Blood Signatures for the Treatment of Rheumatoid Arthritis
TL;DR: A novel computational method is presented for the identification of the applicable and substantial blood gene signatures of IFX sensitivity by liquid biopsy, which may assist in the establishment of a clinical drug sensitivity test standard for RA and contribute to the revelation of unique IFX-associated pharmacological mechanisms.
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Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods
TL;DR: Zhang et al. as discussed by the authors employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in a study using the blood gene expression profiles.