RNA Secondary Structure Prediction using Machine Learning: A Review

Abstract

Complete knowledge of base pairing in RNA secondary structure would open novel avenues in disease treatment and regulation of cellular processes. In pursuit of this objective, probabilistic models, especially those employing machine learning have come to dominate RNA secondary structure prediction, proving better than previous tools which were based upon comparative sequence analysis or folding algorithms employing thermodynamic and stochastic parameter schemes. This review studies landmark models using machine learning techniques, which have accelerated the research in RNA secondary structure prediction in the past two decades. Additionally, an elaboration on RNA types, functions, and their functional mechanisms in diseases, is intended to provide the reader with the prerequisite knowledge to understand the vitality of unearthing structural information of RNA. An outline of alternate RNA structure tools and techniques is also incorporated for a better understanding of the theme.