Chapter 9 - A machine learning approach to bioinformatics

Abstract

Bioinformatics algorithms had to be developed manually long before machine learning, which was difficult for issues like predicting protein structure. Instead of requiring the programmer to describe each characteristic individually, machine learning techniques such as deep learning may learn the characteristics of data sets. The algorithm can get better at combining simple traits into more complicated ones, and so on. Because of their multi-layered architecture, these systems can produce complex predictions when properly trained. Unlike previous approaches to computational biology, these methods prevent data from being interpreted and evaluated in ways that aren't expected. The availability of biological datasets has exploded in recent years. This chapter covers a wide range of topics concerning the use of machine learning techniques in bioinformatics applications. The most popular machine learning techniques in bioinformatics are introduced in the first section, and their application is discussed along with evaluations from real-world case studies. It also covers cutting-edge bioinformatics research methods. The theoretical parts are put together well, so readers can use the same methods in their own research.