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Weixiong Zhang
Researcher at Washington University in St. Louis
Publications - 239
Citations - 10598
Weixiong Zhang is an academic researcher from Washington University in St. Louis. The author has contributed to research in topics: Gene & Local search (optimization). The author has an hindex of 54, co-authored 226 publications receiving 9451 citations. Previous affiliations of Weixiong Zhang include University of Southern California & Shanghai Jiao Tong University.
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Identification of novel and candidate miRNAs in rice by high throughput sequencing.
TL;DR: Deep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate mi RNAs in rice, mostly derived from a unique locus in rice genome.
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Transcriptome-wide identification of microRNA targets in rice
Yong-Fang Li,Yun Zheng,Charles Addo-Quaye,Li Zhang,Ajay Saini,Guru Jagadeeswaran,Michael J. Axtell,Weixiong Zhang,Ramanjulu Sunkar +8 more
TL;DR: In this paper, the authors applied the degradome sequencing approach to identify small RNA targets in rice, which globally identifies the remnants of small RNA-directed target cleavage by sequencing the 5' ends of uncapped RNAs.
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Characterization and identification of microRNA core promoters in four model species
TL;DR: It is shown that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have, and a novel promoter prediction method is developed, called common query voting (CoVote), which is more effective than available promoter prediction methods.
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Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks
TL;DR: The results show that DSA is superior to DBA when controlled properly, having better or competitive solution quality and significantly lower communication cost than DBA, and is the algorithm of choice for distributed scheduling problems and other distributed problems of similar properties.
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Identification of cold-inducible microRNAs in plants by transcriptome analysis.
TL;DR: A computational, transcriptome-based approach to annotating stress-inducible microRNAs in plants finds that nineteen microRNA genes of eleven microRNA families in Arabidopsis thaliana are up-regulated by cold stress, and demonstrates that machine learning methods, augmented by wet-lab analysis, hold a great promise for functional annotation of micro RNAs.