Current Computational Models for Prediction of the Varied Interactions Related to Noncoding RNAs

Copyright © 2016 Xing Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Noncoding RNA (ncRNA) refers to a kind of endogenous small RNA molecules that have no protein coding capacity. In recent years, extensive studies have been conducted to study the roles of ncRNAs in cell biology, and accumulating evidences show that these RNA molecules do not constitute transcriptional noise but play important roles in cellular functions, such as transcriptional and posttranscriptional regulation, epigenetic regulation, organ or tissue development , cell differentiation, cell cycle control, cellular transport , metabolic processes, and chromosome dynamics. Their deregulation heavily contributes to various pathological conditions of human complex diseases, including breast cancer, hepatocellular cancer, prostate cancer, colon cancer, bladder cancer, thyroid cancer, lung cancer, ovarian cancer, leukemia, Alzheimer's disease, diabetes, and HIV. The development of computational models to predict the various complex ncRNA-related interactions could significantly benefit the inference of ncRNA function, the identification of ncRNA-disease associations, the detection of ncRNA biomarker, the identification of drug-target interactions, and drug design. The papers that follow in the next pages describe recent findings in the field of computational models for prediction of the varied interactions related to ncRNAs. They represent only a fraction of the current research, the emerging trends, and applications of computational models for ncRNAs. The special issue consists of eight research papers and one review paper. The research papers include identification and annotation of ncRNAs (four papers), miRNA-target interaction prediction (one paper), ncRNA function prediction (one paper), miRNA-transcriptional factor interaction prediction (one paper), and protein-protein interaction prediction (one paper). A brief description of the papers follows. G. Wan et al. analyzed RNA-seq data and ChIP-seq histone modification data to identify the relationship between lncRNA genes transcription and histone modification H3K4me3 or H3K27me3 in the paper " Transcriptional Regulation of lncRNA Genes by Histone Modification in Alzheimer's Disease. " J. Qu et al. detected 254 small noncoding RNAs in genome of Pleurotus ostreatus and analyzed the evolutionary conservation of them in the paper " Identification and Characterization of Small Noncoding RNAs in Genome Sequences of the Edible Fungus Pleurotus ostreatus. " A meta-path-based prediction method RMLM was developed in the paper " A Meta-Path-Based Prediction Method for Human miRNA-Target Association " by J. Luo et al. for predicting potential …