Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases

Epigenetics in Health and Disease, Volume 197 in the Progress in Molecular Biology and Translational Science series, highlights new advances in the field, with this new volume presenting interesting chapters on An Introduction to epigenetics in health and disease, State-of-the-art techniques for epigenetics study, Computational biology in epigenetics, Artificial intelligence and machine learning in epigenetics, CRISPR-Cas systems in epigenetics modification for health, Mechanisms of DNA methylation and histone modifications, Non-coding RNA in epigenetics, Role of DNA Methylation in cardiovascular diseases, Role of epigenetics in renal diseases, and much more. Additional chapters update on Histone modifications in fat metabolism and obesity, Role of DNA methylation in diabetes and obesity, Epigenetic regulons in Alzheimer’s disease, Epigenetics in heredity disease, Epigenetics of aging, Epigenetics in cancer development, diagnosis and therapy, Microbiota and epigenetics: health impact, Epigenetics in Fetal Alcohol Spectrum disorder, and much more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Progress in Molecular Biology and Translational Science series Includes the latest information on Epigenetics in Health and Disease

Computational Epigenomics and Diseases: Epigenomic Data Analytics for Human Health Application explains the current computational approaches inferring epigenetic mechanisms from epigenetic data. Epigenetic research leads to a considerable amount of data that can be more efficiently organized and analyzed using computer-based systems. All applicable computational approaches are explained in detail within this volume. Computational Epigenetics discusses topics such as statistical analysis and management of big epigenetics datasets; relationships among epigenetic factors and diseases; computational inference of spatial organization of genome; differential regulations and inference of variations of chromatin modifications; and systems biology approaches for identifying chromatin regulators. Additionally, strategies for applying epigenetics data analysis results to disease diagnosis, prognosis, and case studies are included in order to provide thorough and translational comprehension and applicability. The book is a valuable resource for computer scientists, mathematicians, and statisticians interested in bioinformatics and computational biology approaches to epigenetic data analysis, as well as geneticists who are looking to improve their knowledge of computational analytics for their research. Explains the computational methods inferring features of epigenetic marks; Describes the basic computational methods for understanding and deciphering chromatin signatures at the primary organization level; Offers example publications and case studies to show the range of possible applications of the computational analyses of epigenetics data.

This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike. Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.