Biomaterial Database

Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses. 2020

Keyvan Asefpour Vakilian

Department of Agrotechnology, College of Abouraihan, University of Tehran, Tehran, Iran. keyvan.asefpour@ut.ac.ir.

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.

UI	MeSH Term	Description	Entries
D008954	Models, Biological	Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.	Biological Model,Biological Models,Model, Biological,Models, Biologic,Biologic Model,Biologic Models,Model, Biologic
D012044	Regression Analysis	Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.	Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D000069550	Machine Learning	A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.	Transfer Learning,Learning, Machine,Learning, Transfer
D013312	Stress, Physiological	The unfavorable effect of environmental factors (stressors) on the physiological functions of an organism. Prolonged unresolved physiological stress can affect HOMEOSTASIS of the organism, and may lead to damaging or pathological conditions.	Biotic Stress,Metabolic Stress,Physiological Stress,Abiotic Stress,Abiotic Stress Reaction,Abiotic Stress Response,Biological Stress,Metabolic Stress Response,Physiological Stress Reaction,Physiological Stress Reactivity,Physiological Stress Response,Abiotic Stress Reactions,Abiotic Stress Responses,Abiotic Stresses,Biological Stresses,Biotic Stresses,Metabolic Stress Responses,Metabolic Stresses,Physiological Stress Reactions,Physiological Stress Responses,Physiological Stresses,Reaction, Abiotic Stress,Reactions, Abiotic Stress,Response, Abiotic Stress,Response, Metabolic Stress,Stress Reaction, Physiological,Stress Response, Metabolic,Stress Response, Physiological,Stress, Abiotic,Stress, Biological,Stress, Biotic,Stress, Metabolic
D017360	Arabidopsis	A plant genus of the family BRASSICACEAE that contains ARABIDOPSIS PROTEINS and MADS DOMAIN PROTEINS. The species A. thaliana is used for experiments in classical plant genetics as well as molecular genetic studies in plant physiology, biochemistry, and development.	Arabidopsis thaliana,Cress, Mouse-ear,A. thaliana,A. thalianas,Arabidopses,Arabidopsis thalianas,Cress, Mouse ear,Cresses, Mouse-ear,Mouse-ear Cress,Mouse-ear Cresses,thaliana, A.,thaliana, Arabidopsis,thalianas, A.
D018515	Plant Leaves	Expanded structures, usually green, of vascular plants, characteristically consisting of a bladelike expansion attached to a stem, and functioning as the principal organ of photosynthesis and transpiration. (American Heritage Dictionary, 2d ed)	Plant Leaf,Leaf, Plant,Leave, Plant,Leaves, Plant,Plant Leave
D035683	MicroRNAs	Small double-stranded, non-protein coding RNAs, 21-25 nucleotides in length generated from single-stranded microRNA gene transcripts by the same RIBONUCLEASE III, Dicer, that produces small interfering RNAs (RNA, SMALL INTERFERING). They become part of the RNA-INDUCED SILENCING COMPLEX and repress the translation (TRANSLATION, GENETIC) of target RNA by binding to homologous 3'UTR region as an imperfect match. The small temporal RNAs (stRNAs), let-7 and lin-4, from C. elegans, are the first 2 miRNAs discovered, and are from a class of miRNAs involved in developmental timing.	RNA, Small Temporal,Small Temporal RNA,miRNA,stRNA,Micro RNA,MicroRNA,Primary MicroRNA,Primary miRNA,miRNAs,pre-miRNA,pri-miRNA,MicroRNA, Primary,RNA, Micro,Temporal RNA, Small,miRNA, Primary,pre miRNA,pri miRNA