Machine learning for Big Data analytics in plants

Chuang Ma, Hao Helen Zhang, Xiangfeng Wang

Research output: Contribution to journalReview article

84 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)798-808
Number of pages11
JournalTrends in Plant Science
Volume19
Issue number12
DOIs
StatePublished - Dec 1 2014

Keywords

  • Big Data
  • Large-scale datasets
  • Machine learning
  • Plants

ASJC Scopus subject areas

  • Plant Science

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