title: Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms.
abstract: 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/.
authors.affiliation: Department of Biomedical Informatics, Asia University, Taichung 41354, Taiwan; School of Information Technology, Mea Fah Luang University, 57100 Thailand. Electronic address: sendtoopal@gmail.com.
authors.forename: Nilubon
authors.initials: N
authors.lastname: Kurubanjerdjit
authors.forename: Chien-Hung
authors.initials: CH
authors.lastname: Huang
authors.forename: Yu-Liang
authors.initials: YL
authors.lastname: Lee
authors.forename: Jeffrey J P
authors.initials: JJ
authors.lastname: Tsai
authors.forename: Ka-Lok
authors.initials: KL
authors.lastname: Ng
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism / Q000502:physiology; D029681:Arabidopsis Proteins / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003663:Decision Trees; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D015233:Models, Statistical; D060066:Protein Interaction Maps / Q000235:genetics*; D060388:Support Vector Machine
chemical_list: D029681:Arabidopsis Proteins; D035683:MicroRNAs
keywords: Arabidopsis thaliana; Enrichment analysis; Jaccard coefficient; Machine learning algorithm; Protein–protein interaction; microRNA target prediction