Computational biology refers to the study of behavioral, biological, social and ecological systems through the implementation of theoretical and data-analytical methods, computational simulation approaches, and mathematical modeling. It involves the study of biological data in order to create models or algorithms, which can be used to better understand biological systems and relationships. It also assists in the sequencing of the human genome, modeling of biological systems, constructing accurate models of the human brain, and mapping the 3D structure of genomes. There are numerous methods which are used within this field such as unsupervised learning, supervised learning and graph analytics. Unsupervised learning refers to a type of algorithm which is used to find patterns in unlabeled data. This book provides comprehensive insights into the field of computational biology. It presents researches and studies performed by experts across the globe. This book aims to serve as a resource guide for students and experts alike and contribute to the growth of computational biology.