Abstract
During the last two decades, human has increased his knowledge about the role of miRNAs and their target genes in plant stress response. Biotic and abiotic stresses result in simultaneous tissue-specific up/down-regulation of several miRNAs. In this study, for the first time, feature selection algorithms have been used to investigate the contribution of individual plant miRNAs in Arabidopsis thaliana response towards different levels of several abiotic stresses including drought, salinity, cold, and heat. Results of information theory-based feature selection revealed that miRNA-169, miRNA-159, miRNA-396, and miRNA-393 had the highest contributions to plant response towards drought, salinity, cold, and heat, respectively. Furthermore, regression models, i.e., decision tree (DT), support vector machines (SVMs), and Naïve Bayes (NB) were used to predict the plant stress by having the plant miRNAs’ concentration. SVM with Gaussian kernel was capable of predicting plant stress (R2 = 0.96) considering miRNA concentrations as input features. Findings of this study prove the performance of machine learning as a promising tool to investigate some aspects of miRNAs’ contribution to plant stress responses that have been undiscovered until today.
Highlights
During the last two decades, human has increased his knowledge about the role of miRNAs and their target genes in plant stress response
A large part of these studies has focused on Arabidopsis thaliana, Brachypodium distachyon, Glycine max, Hordeum vulgare, Medicago truncatula, Manihot esculenta, Phaseolus vulgaris, Populus euphratica, Populus trichocarpa, Populus tremula, Triticum turgidum, Oryza sativa, Vigna unguiculate, and Zea mays[15]
This study is the first report of using machine learning to investigate the contribution of miRNAs in plant stress response
Summary
During the last two decades, human has increased his knowledge about the role of miRNAs and their target genes in plant stress response. Biotic and abiotic stresses result in simultaneous tissue-specific up/down-regulation of several miRNAs. In this study, for the first time, feature selection algorithms have been used to investigate the contribution of individual plant miRNAs in Arabidopsis thaliana response towards different levels of several abiotic stresses including drought, salinity, cold, and heat. Only involved miRNAs, their expression in stress conditions, and their target genes are already identified in previous studies and their contribution to plant response towards different levels of plant stress is still a matter of question. The preparation of a database based on the observations of miRNA expressions at different levels of plant stress can be the first step Methods such as northern blot and polymerase chain reaction (PCR) which have been widely used to measure miRNA expressions suffer from weak analytical characteristics, e.g., limit of detection, response linear range, and precision[22]. Afterwards, using feature selection algorithms to rank the miRNAs will be a possible solution in miRNA contribution investigations
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