7 Papers
Man Hu is an academic researcher from China Agricultural University. The author has contributed to research in topics: Biology & Chemistry. The author has an hindex of 2, co-authored 2 publications.
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Papers
Generation of Pigs Resistant to Highly Pathogenic-Porcine Reproductive and Respiratory Syndrome Virus through Gene Editing of CD163.
Chen Jingyao,Haitao Wang,Jianhui Bai,Wenjie Liu,Xiaojuan Liu,Dawei Yu,Tao Feng,Sun Zhaolin,Linlin Zhang,Linyuan Ma,Yiqing Hu,Yunlong Zou,Tan Tan,Jie Zhong,Man Hu,Xiaofei Bai,Dengke Pan,Yiming Xing,Yaofeng Zhao,Kegong Tian,Kegong Tian,Xiaoxiang Hu,Ning Li +22 more
TL;DR: Modifying CD163 remarkably inhibited PRRSV replication and protected pigs from HP-PRRSV infection, thus establishing a good foundation for breeding PR RSV-resistant pigs via gene editing technology.
Screen and Verification for Transgene Integration Sites in Pigs
Linyuan Ma,Yuzhe Wang,Haitao Wang,Yiqing Hu,Chen Jingyao,Tan Tan,Man Hu,Xiaojuan Liu,Ran Zhang,Yiming Xing,Yiqiang Zhao,Xiaoxiang Hu,Ning Li +12 more
TL;DR: It is anticipated that Pifs501 will have useful applications in pig genome engineering, though the identification of genomic safe harbor should over long-term various functional studies be identified.
A Random Forest Model for Peptide Classification Based on Virtual Docking Data
Hua Feng,Fang Wang,Ning Li,Qian Xu,Guanmin Zheng,Xuefeng Sun,Man Hu,Guangxu Xing,Gaiping Zhang +8 more
TL;DR: In this paper , the authors employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized.
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Virtual Screening-Based Peptides Targeting Spike Protein to Inhibit Porcine Epidemic Diarrhea Virus (PEDV) Infection
Qian Xu,Fang Wang,Wenqiang Jiao,Mengting Zhang,Guangxu Xing,Hua Feng,Xuefeng Sun,Man Hu,Gaiping Zhang +8 more
TL;DR: Wang et al. as discussed by the authors used a molecular docking technology for virtual screening of affinity peptides that specifically recognized the PEDV S1 C-terminal domain (CTD) protein for the first time.
Use of tree-based machine learning methods to screen affinitive peptides based on docking data.
Hua Feng,Fangyu Wang,Ning Li,Qian Xu,Guanming Zheng,Xuefeng Sun,Man Hu,Xuewu Li,Guangxu Xing,Gaiping Zhang +9 more
TL;DR: A tree‐model competition on affinitive peptide prediction by using virtual docking data is given, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.
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