Elsevier Introduction to Bioinformatics and Machine Learning

Introduction to Bioinformatics and Machine Learning provides comprehensive instruction in modern computational biology by integrating bioinformatics, genomics, artificial intelligence, and machine learning into a unified learning framework. The book bridges the gap between biological data analysis and computational methods, covering fundamental concepts in molecular biology and bioinformatics together with practical applications of machine learning to genomics, transcriptomics, proteomics, microbiome research, structural biology, NGS technologies, liquid biopsy, cancer genomics, and precision medicine. Readers are introduced to DNA and RNA sequencing technologies, genome assembly, sequence alignment, variant discovery, gene expression analysis, fusion gene detection, protein structure prediction, biological databases, statistical analysis, explainable artificial intelligence, and predictive modeling. Throughout the book, real-world biomedical case studies demonstrate how computational methods are transforming modern biological and clinical research; and both bioinformatics and machine learning are addressed in a coherent and accessible manner, emphasizing practical problem-solving, reproducible computational workflows, and biological interpretation of results. Designed for upper-level undergraduate students, graduate students, and researchers, this book serves as both a classroom textbook and practical assignments for those working at the interface of biology, computer science, medicine, and data science. Integrates bioinformatics, genomics, artificial intelligence, and machine learning within a single educational framework Covers modern sequencing technologies, including whole-genome sequencing, RNA sequencing, genomics, and long-read sequencing Introduces computational analysis of multi-omics data, cancer genomics, and precision medicine Includes practical examples, real biological datasets, and biomedical case studies Provides step-by-step tutorials using widely adopted open-source bioinformatics databases, software tools, and programming environments Features exercises, review questions, lecture slides, practical assignments, datasets, and online supplementary material