Chapter 12 - Machine-Learning Approach for Ribonucleic Acid Primary and Secondary Structure Prediction from Images

Abstract

Ribonucleic acid (RNA) primary and secondary structure prediction and analysis are of utmost importance for assessing the biological organ functionalities. Several RNA structure prediction methods use pattern recognition and artificial intelligence approaches. However, several machine-learning approaches, such as Hidden Markov model, are more interpretable and accurate for an improved structured analysis compared to the artificial intelligence approaches. They employ graphical data analysis to enhance the prediction accuracy. This chapter proposes an RNA prediction approach from RNA images based on hidden Markov model and Chapman Kolmogorov equation with filtering process. Initially, Gaussian filter, box filter, and median filter are applied during the filtering stage. Furthermore, Otsu's technique is applied to convert the RNA image into binary image as well as binary matrix. Rank transformation as well as Box and cox transformations are used for binary matrix optimization. In order to classify the RNA structures, Flood fill and Warshall are employed for counting. Finally, Hidden Markov model and Chapman Kolmogorov equations are applied on the classified secondary and tertiary structures of RNA structure prediction.