Machine Learning and Deep Learning in Bioinformatics

The current era is experiencing an exponential growth in the availability of enormous datasets and sophisticated databases in bioinformatics. The emergence of complex data requires integrating newer strategies to extract valuable information from high-throughput datasets and convert them to tangible forms for easier, faster, and more accurate data interpretation. Machine learning comprises training models that facilitate the streamlined extraction of data and predictive model-based understanding in the domains of genomics, proteomics, microarray, and system biology. Additionally, deep learning uses layers of neural networks to mimic the function of the human brain to decipher optimally tuned outcomes by detailed analysis of large amounts of complex, unstructured, and randomly organized information. These tools have performed unprecedented data analysis using multiplex datasets to identify DNA-protein binding motifs and protein-ligand binding and gained insights from intricate genome sequences, pattern recognition of diseases-associated gene expression variants, biomarkers, and biomedical image analysis revolutionized disease diagnosis and drug discovery. This chapter will discuss various machine learning models (Bayesian, Gaussian, and Markov models) and deep neural network strategies such as artificial neural networks (convolutional and recurrent), autoencoders, and several others in bioinformatics and their applications in biomedical advancements in academia and industry.