title: Machine learning for Big Data analytics in plants.
abstract: Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.
authors.affiliation: School of Plant Sciences, University of Arizona, 1140 E. South Campus Drive, Tucson, AZ 85721, USA.
authors.forename: Chuang
authors.initials: C
authors.lastname: Ma
authors.affiliation: Department of Mathematics, University of Arizona, 617 North Santa Rita Ave, Tucson, AZ 85721, USA.
authors.forename: Hao Helen
authors.initials: HH
authors.lastname: Zhang
authors.affiliation: School of Plant Sciences, University of Arizona, 1140 E. South Campus Drive, Tucson, AZ 85721, USA; Department of Plant Genetics and Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China. Electronic address: xwang1@cals.arizona.edu.
authors.forename: Xiangfeng
authors.initials: X
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D010944:Plants / Q000235:genetics / Q000378:metabolism*; D012984:Software
keywords: Big Data; large-scale datasets; machine learning; plants