Journal
TRENDS IN PLANT SCIENCE
Volume 19, Issue 12, Pages 798-808Publisher
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tplants.2014.08.004
Keywords
Big Data; machine learning; large-scale datasets; plants
Categories
Funding
- U.S. National Science Foundation [DBI-1261830]
- NSF [DMS-1309507, DMS-1418172]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1261830] Funding Source: National Science Foundation
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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.
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