Chuang Ma

  

  Prof. Ma, Chuang

  

  Contact Information:

  Mailing Address:

  College of Life Sciences

  Northwest A&F University

  Yangling, Shaanxi 712100

  P. R. China

  Phone: (86) 29 87091109

  Fax: (86) 29 87091109

  E-mail: cma@nwafu.edu.cn

  Office: North Campus, Science Building, College of Life Sciences, D501

  

  Research Interests:

  1. Artificial Intelligence: Large-scale integration and mining of ‘omics’ data with machine learning technologies.

  2. Abiotic Stress: Bioinformatics and systems biology approaches to understand molecular mechanisms by which plants respond to and mount resistance to environmental stresses.

  3. Plant Breeding: Bioinformatics and systems biology approaches to map genotyping to phenotyping for genomic selection (GS)-assisted plant breeding.

  

  Education Experiences:

  Huazhong University of Science & Technology, Bioinformatics Technology, Ph.D. 2010

  Huazhong University of Science & Technology, Bioinformatics Technology, M.S. 2007

  Huazhong University of Science & Technology, Applied Physics, B. S. 2004

  

  Professional Experiences:

  Professor in College of Life Sciences, Norwest A&F University (2014-present)

  Postdoc fellow in The University of Arizona (2011-2014)

  Postdoc fellow in The University of Southern California (2010-2011)

  

  Honors and Awards:

  The Talents Plan of China (for Young Professionals), 2016

  The Talent Plan of Shaanxi Province of China, 2016

  The ‘Youth Science & Technology Star’ of Shaanxi Province of China, 2016

  

  Publications:

  Miao Z, Han Z, Zhang T, Chen S and Ma C. (2017) A systems approach to a spatio-temporal understanding of the drought stress response in maize. Scientific Reports 7:6590.

  Zhai J, Tang Y, Yuan H, Wang L, Shang H and Ma C. (2016) A meta-analysis based method for prioritizing candidate genes involved in a pre-specific function. Frontiers in Plant Science 7:1914.

  Qu J, Ma C, Feng J, Xu S, Wang L, Li F, Li Y, Zhang R, Zhang X, Xue J, Guo D. (2016) Transcriptome dynamics during maize endosperm development. Plos One 11: e0163814.

  Cui H, Zhai J, Ma C. (2015) miRLocator: Machine learning-based prediction of mature microRNAs within plant pre-miRNA sequences. Plos One 10: e0142753.

  Zhan J, Thakare D, Ma C, Lloyd A, Nixon NM, Burnett WJ, Logan KO, Wang D, Wang X, Drews GN, Yadegari R. (2015) RNA sequencing of laser-capture microdissected compartments of the maize kernel identified regulatory modules associated with endosperm cell differentiation. The Plant Cell 27:513-531.

  Ma C, Zhang HH, Wang X. (2014) Machine learning for big data analytics in plants. Trends in Plant Science 19: 798-808.

  Ma C, Xin M, Feldmann KA, Wang X. (2014) Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis . The Plant Cell 26: 520-537.

  Ma C and Wang X. (2012) Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. Plant Physiology 160: 192-203.

  Ma C, Chen H, Xin M, Yang R, Wang X. (2012) KGBassembler: A karyotype-based genome assembler for Brassicaceae species. Bioinformatics 28: 3141-3143

  

  

  

  

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