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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Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.
Lei Chen,Lei Chen,Yu-Hang Zhang,Guohua Huang,Xiaoyong Pan,ShaoPeng Wang,Tao Huang,Yu-Dong Cai +7 more
TL;DR: The sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs, and an effective classification model to distinguish them was built.
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The prediction of interferon treatment effects based on time series microarray gene expression profiles
TL;DR: A time-dependent diagnostic model is proposed to predict the treatment effects of interferon and ribavirin to HCV infected patients by using time series microarray gene expression profiles of a published study and could correctly predict all Caucasian American patients' treatment effects at very early time point.
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Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection
TL;DR: In this article, using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases.
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Classifying Ten Types of Major Cancers Based on Reverse Phase Protein Array Profiles
TL;DR: A computational framework to classify the patient samples into ten major cancer types based on the RPPA data using the SMO (Sequential minimal optimization) method and the analysis of these 23 proteins lends credence to the importance of these genes as indicators of cancer classification.
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Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.
TL;DR: Analysis of data retrieved from patients who had one of six cancer subtypes as well as healthy persons indicated that these genes could be important biomarkers for discriminating different cancer sub types and healthy controls.