A General Computational Framework for Prediction of Disease-associated Non-coding RNAs

Abstract

Since last decade, we have been witnessing the raise of non-coding RNAs (ncRNAs) in biomedical research. Many ncRNAs have been identified and classified into different classes based on their length in number of base pairs (bp). In parallel, our understanding about functions of ncRNAs is gradually increased. However, only small set among tens of thousands of ncRNAs have been well studied about their functions and their roles in development of diseases. This raises a pressing need to develop computational methods to associate diseases and ncRNAs. Two most widely studied ncRNAs are microRNA (miRNA) and long non-coding RNA (lncRNA), since miRNAs are the regulators of most protein-coding genes and lncRNAs are the most ubiquitously found in mammalian. To date, many computational methods have been also proposed for prediction of disease-associated miRNAs and lncRNAs, and recently comprehensively reviewed. However, in the previous reviews, these computational methods were described separately, thus this limits our understanding about their underlying computational aspects. Therefore, in this study, we propose a general computational framework for prediction of disease-associated ncRNAs. The framework demonstrates a whole computational process from data preparation to computational models.