Fundamentals of Bioinformatics and Computational Biology: Methods and Exercises in MATLAB (Second 20

This book comprehensively covers all the core bioinformatics topics and includes practical examples completed using the MATLAB bioinformatics and machine learning toolboxes(TM). It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics to enable physical science students to appreciate the challenges in biological data management, sequence analysis, and systems biology. The book is divided into five parts. The first one includes a survey of existing biological databases and tools that have become essential in today's biotechnology research. The second part covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The third part of the book focuses on modeling biological sequences and patterns as Markov chains, covering core principles for analyzing and searching for sequences of significant motifs and biomarkers and developing stochastic ergodic hidden Markov models for biological sequence families. The fourth one is dedicated to systems biology and covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In turn, the last part of the book includes an introduction to machine-learning algorithms for bioinformatics and outlines strategies for developing intelligent diagnostic machine-learning applications, RNA sequence data, and deep learning systems for mass spectrometry data. All in all, this book offers a unique hands-on reference guide to bioinformatics and computational biology. This second edition has been updated to cover additional and most recent databases, and machine learning and deep learning applications in RNA sequence and mass-spectrometry data analysis. Moreover, it presents significant enhancements to the chapter dedicated to microarray analysis, and more practical examples, with additional end-of-chapter problems.