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Guanxun Li

Texas A&M University - Department of Statistics

155 Ireland Street

447 Blocker

College Station, TX 77843

United States

  • Texas A&M University - Department of Statistics

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scTenifoldKnk: An Efficient Virtual Knockout Tool for Gene Function Predictions via Single-Cell Gene Regulatory Network Perturbation

Number of pages: 96 Posted: 23 Jun 2021
Daniel Osorio, Yan Zhong, Guanxun Li, Qian Xu, Yongjian Yang, Andrew Hillhouse, Jingshu Chen, Laurie A. Davidson, Yanan Tian, Robert S. Chapkin, Jianhua Z. Huang and James J. Cai
Texas A&M University - Department of Veterinary Integrative Biosciences, Texas A&M University - Department of Statistics, Texas A&M University - Department of Statistics, Texas A&M University - Department of Veterinary Integrative Biosciences, Texas A&M University - Department of Electrical and Computer Engineering, Texas A&M University - Department of Veterinary Integrative Biosciences, Texas A&M University - Department of Veterinary Physiology and Pharmacology, Texas A&M University - Department of Nutrition, Texas A&M University - Department of Veterinary Physiology and Pharmacology, Texas A&M University - Department of Nutrition, The Chinese University of Hong Kong (CUHK) and Texas A&M University - Department of Veterinary Integrative Biosciences
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virtual knockout, gene knockout, gene function prediction, single-cell RNA sequencing, gene regulatory network, functional genomics, unsupervised machine learning