Machine Learning for Bioinformatics

Machine learning (ML) deals with the automated learning of machines without being programmed explicitly. It focuses on performing data-based predictions and has several applications in the field of bioinformatics. Bioinformatics involves the processing of biological data using approaches based on computation and mathematics. The biological data has grown exponentially in recent times leading to two issues. One issue is of efficient information storage and the second issue deals with how useful knowledge can be mined from the data. The second issue can be solved using machine learning which can generate knowledge from data that is heterogeneous in nature. The feature learning is enabled automatically by deep learning which represents a machine learning technique. New set of features are constructed by combining multiple features based on the dataset. This approach enables algorithms to perform complex predictions on large datasets. ML is currently being applied in six key subfields of bioinformatics such as microarrays, evolution, systems biology, genomics, text mining, and proteomics. This chapter is composed of four sections. The first section will provide an outline of ML in bioinformatics. This is followed by the second section which highlights the different machine learning techniques in bioinformatics. The third section describes two case studies using artificial neural network in bioinformatics. The fourth section analyzes the various research areas related to bioinformatics that can be explored by the academicians and researchers. The conclusion of the chapter is presented in the end.