Providing genome-informed personalized treatment is an important goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This book is intended for statisticians who are interested in modeling and analyzing high-throughput data. It covers the development and application of rigorous statistical methods (Bayesian and non-Bayesian) in the analysis of high-throughput bioinformatics data that arise from problems in medical and cancer research and molecular and structural biology. The specific focus of the volume is to provide an overview of the current state of the art of methods to integrate novel high-throughput multiplatform bioinformatics data, for a better understanding of the functional consequences of genomic alterations. The introductory description of biological and technical principles behind multiplatform high-throughput experimentation may be helpful to statisticians who are new to this research area.