Bioinformatics Methods: From Omics to Next Generation Sequencing (Chapman & Hall/CRC Mathematical and Computational Biology)

The past three decades have witnessed an explosion of what is now referred to as high-dimensional `omics' data. Bioinformatics Methods: From Omics to Next Generation Sequencing describes the statistical methods and analytic frameworks that are best equipped to interpret these complex data and how they apply to health-related research. Covering the technologies that generate data, subtleties of various data types, and statistical underpinnings of methods, this book identifies a suite of potential analytic tools, and highlights commonalities among statistical methods that have been developed.

An ideal reference for biostatisticians and data analysts that work in collaboration with scientists and clinical investigators looking to ensure rigorous application of available methodologies.

Key Features:

• Survey of a variety of omics data types and their unique features
• Summary of statistical underpinnings for widely used omics data analysis methods
• Description of software resources for performing omics data analyses

Shili Lin, PhD is a Professor in the Department of Statistics and a faculty member in the Translational Data Analytics Institute at the Ohio State University. Her research interests are in statistical methodologies for high-dimensional and big data, with a focus on their applications in biomedical research, statistical genetics and genomics, and integration of multiple omics data.

Denise Scholtens, PhD is Professor and Chief of the Division of Biostatistics in the Department of Preventive Medicine at Northwestern University Feinberg School of Medicine. She is interested in the design and conduct of large-scale multi-center prospective health research studies, and in the integration of high-dimensional omics data analyses into these settings.

Sujay Datta, PhD is an Associate Professor and the Graduate Program Coordinator in the Department of Statistics at the University of Akron. His research interests include statistical analyses of high-dimensional and high-throughput data, graphical and network-based models, statistical models and methods for cancer data, as well as sequential/multistage sampling designs.