Machine learning and its applications in plant molecular studies

Abstract

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies.

Identifying Antifreeze Proteins Based on Key Evolutionary Information

“…Support vector machine is a popular classifier which has solved several bioinformatics problems (Li et al, 2016;Chen et al, 2017;Bu et al, 2018;Zhang et al, 2018;Chao et al, 2019a,b;Sun et al, 2019;Wang et al, 2019). The "caret" R package was used to train models and tune the model hyperparameters based on SVM (Kuhn, 2008).…”

Machine learning in plant science and plant breeding

“…We also critically discuss applications of ML and indicate current and future research directions. For more in-depth reviews on specific aspects, we refer the reader to the following studies: ( van Eeuwijk et al, 2019 ; Singh et al, 2016 , Singh et al, 2018 ; Mochida et al, 2019 ), focused on traits and phenotyping; ( Sperschneider, 2019 ), focused on using ML in the context of plant-pathogen interactions; ( Sun et al, 2019 ), focused on applying ML at the molecular level in plants; and ( Wang et al, 2020 ), focused on application of ML in plant genomics. For more general reviews, see ( Zou et al, 2019 ) for a summary on deep learning (DL), a specific form of ML, in genomics and to ( Gazestani and Lewis, 2019 ) for an overview of the use of ML to connect genotypes to phenotypes.…”

A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels

“…Next-generation high-throughput sequencing technology has created great wide-ranging data sets. This gigantic data expanse helps biologists to analyze and conduct daunting gene transcripts, such as diseaseassociated and RNA such as diseases (malaria), cancer, inherited, genomic, physiological, among others [1]. Blood-sucking mosquitoes such as mosquito anopheles with main Plasmodium falciparum malaria vectors are found mostly in Africa.…”