Hai Wang
China Agricultural University
44 Papers
101 Citations
Hai Wang is an academic researcher from China Agricultural University. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 14, co-authored 40 publications. Previous affiliations of Hai Wang include Donald Danforth Plant Science Center & Hong Kong Baptist University.
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Papers
Genome-wide selection and genetic improvement during modern maize breeding.
Baobao Wang,Zechuan Lin,Xin Li,Yongping Zhao,Zhao Binbin,Guangxia Wu,Xiaojing Ma,Hai Wang,Yurong Xie,Quanquan Li,Guangshu Song,Dexin Kong,Zhigang Zheng,Hongbin Wei,Rongxin Shen,Hong Wu,Cuixia Chen,Zhaodong Meng,Tianyu Wang,Yu Li,Xinhai Li,Yanhui Chen,Jinsheng Lai,Matthew B. Hufford,Jeffrey Ross-Ibarra,Hang He,Haiyang Wang +26 more
TL;DR: This work conducted a comprehensive analysis of the genomic and phenotypic changes associated with modern maize breeding through chronological sampling of 350 elite inbred lines representing multiple eras of germplasm from both China and the United States to demonstrate the use of the breeding-era approach for identifying breeding signatures.
235
The microbial metabolite trimethylamine N-oxide promotes antitumor immunity in triple-negative breast cancer.
Hai Wang,Xing-Yu Rong,Gan Zhao,Yifan Zhou,Yi Xiao,Ding Ma,Xi Jin,Yonglin Wu,Yuchen Yan,Hao Yang,Yuan Zhou,Manning Qian,Chen Niu,Xin Hu,Da-Qiang Li,Qingyun Liu,Yumei Wen,Yi-Zhou Jiang,Chao Zhao,Zhi Ming Shao +19 more
TL;DR: Borghaei et al. as mentioned in this paper performed a multi-omics analysis of patients with TNBC and found genera under Clostridiales, and the related metabolite trimethylamine Noxide (TMAO) was more abundant in tumors with an activated immune microenvironment.
199
Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence
Jacob D. Washburn,Maria Katherine Mejia Guerra,Guillaume P. Ramstein,Karl A. Kremling,Ravi Valluru,Edward S. Buckler,Hai Wang +6 more
TL;DR: In this article, two approaches that account for evolutionary relatedness in machine learning models were developed: (i) gene-family-guided splitting and (ii) ortholog contrasts, and the two approaches were explored and validated within the context of mRNA expression level prediction.
162
Deep learning for plant genomics and crop improvement.
TL;DR: A central role of deep learning is proposed in future plant genomics research and crop genetic improvement and the possibility of unleashing the power ofdeep learning in synthetic biology to create novel genomic elements with desirable functions is discussed.
149
Identification of redox-sensitive cysteines in the arabidopsis proteome using OxiTRAQ, a quantitative redox proteomics method
TL;DR: This approach allows identification of the specific redox‐regulated cysteine residues, and offers an effective tool for elucidation of redox proteomes.
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