Comput. Biol. Med. - Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms.

Tópicos

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Resumo

MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/At_miRNA/.

Resumo Limpo

microrna small endogen rnas found mani differ speci known influenc divers biolog phenomena also play crucial role plant biolog process metabol leaf sided flower develop howev function role microrna still unknown identif close relat microrna target gene can essenti first step toward discoveri combinatori effect differ cellular state lot research tri discov microrna target gene interact implement machin learn classifi target predict algorithm howev high rate fals posit report result undetermin factor will affect recognit therefor integr divers techniqu improv predict paper propos identifi microrna target arabidopsi thaliana integr predict score pita miranda rnahybrid algorithm use featur vector micrornatarget interact implement svm random forest tree neural network machin learn algorithm make final predict major vote furthermor microrna target gene link proteinprotein interact ppi partner focus plant resist gene transcript factor inform provid new insight plant pathogen interact network downstream pathway character jaccard coeffici implement base gene ontolog databas freeli access httpppibioinfoasiaedutwatmirna

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