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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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Is Sedentary Behavior Associated With Executive Function in Children and Adolescents? A Systematic Review

TL;DR: The available evidence on the associations between sedentary behavior and executive function is not conclusive in children and adolescents, however, screen-based sedentarybehavior may be negatively associated with executive function.
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Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.

TL;DR: In this article, the authors used the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization to construct models, which can help promote the development of predictive technologies on sub-cellular localizations and provide a new approach for exploring the protein subcellsular localization patterns and their potential biological importance.
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Alternative Polyadenylation Modification Patterns Reveal Essential Posttranscription Regulatory Mechanisms of Tumorigenesis in Multiple Tumor Types.

TL;DR: The key APA-modified genes had a potential prognosis ability Because of their significant power in the survival analysis of TCGA pan-cancer data, the classifier can classify cancer patients into cancer types with perfect performance.
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A meta-analysis of genome-wide gene expression differences identifies promising targets for type 2 diabetes mellitus

TL;DR: The study highlighted the important markers for diabetes mellitus that have shown interaction with other proteins having a role in the progression of diabetes mell Titus that can serve as new targets in the management of DM.
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Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods

TL;DR: In this article , Wang et al. constructed efficient classifiers and identified essential gene biomarkers based on single-cell RNA sequencing data through Boruta and three feature ranking algorithms (e.g., mRMR, MCFS, and SHAP by LightGBM) by utilizing four advanced classification algorithms.