Machine learning: A powerful tool for gene function prediction in plants

• Published in: Applications in plant sciences Vol. 8; no. 7; pp. e11376 - n/a
• Main Authors: Mahood, Elizabeth H., Kruse, Lars H., Moghe, Gaurav D.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.07.2020; John Wiley and Sons Inc; Wiley
• Subjects: Algorithms; big data; bioinformatics; Classification; Decision trees; Deep learning; Diploids; Gene expression; gene function; Genomes; Genomics; Learning algorithms; Machine learning; Neural networks; Ontology; Phenotypes; Plant sciences; Predictions; predictive genomics; Principal components analysis; Proteins; Review; Support vector machines; predictive genomics; gene function; big data; machine learning; bioinformatics
• ISSN: 2168-0450; 2168-0450
• DOI: 10.1002/aps3.11376

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule‐based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning–based approaches in plants.