edited by Joseph Loscalzo, Albert-László Barabási and Edwin K. Silverman
contributions by John C. Earls, James A. Eddy, Samik Ghosh, Kimberly R. Glass, Jeremy Gunawardena, David E. Hill, Isabelle Huvent, Hiroaki Kitano, Isabelle Landrieu, Jessica Lasky-Su, Arnaud Leroy, Guy Lippens, Augusto A. Litonjua, Douglas Luke, Shuyi Ma, Calum A. MacRae, Jörg Menche, Sucheendra K. Palaniappan, Benjamin Parent, Sudhakaran Prabakaran, Nathan Price, John Quackenbush, Thomas Rolland, Martin W. Schoen, Caroline Smet-Nocca, Joanne E. Sordillo, Marc Vidal, George M. Weinstock, Scott Tillman Weiss, Elliott M. Antman, Michael A. Calderwood, Benoit Charloteaux, Clary B. Clish, Michael E. Cusick and Dawn Lisa Demeo
Harvard University Press, 2017
eISBN: 978-0-674-54553-3 | Cloth: 978-0-674-43653-4
Library of Congress Classification R858.N48 2016
Dewey Decimal Classification 610.285


Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. Network Medicine introduces this rapidly evolving field of medical research, which promises to revolutionize the diagnosis and treatment of human diseases. With contributions from leading experts that highlight the necessity of a team-based approach in network medicine, this definitive volume provides readers with a state-of-the-art synthesis of the progress being made and the challenges that remain.

Medical researchers have long sought to identify single molecular defects that cause diseases, with the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the inherent complexity of human diseases and has often led to treatments that are inadequate or fraught with adverse side effects. Rather than trying to force disease pathogenesis into a reductionist model, network medicine embraces the complexity of multiple influences on disease and relies on many different types of networks: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples. The authors offer a systematic approach to understanding complex diseases while explaining network medicine’s unique features, including the application of modern genomics technologies, biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in an integrative context.

By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease.

See other books on: Complex Systems | Data processing | Diagnosis | Diseases | Medical informatics
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