Book Offers Intro to Rapidly Growing Field of Topological Data Analysis
January 31, 2020
The deluge of data in the diverse field of biology comes with it the challenge of extracting meaningful information from large biological data sets. A new book, Topological Data Analysis for Genomics and Evolution, introduces central ideas and techniques of topological data analysis and aims to explain in detail a number of specific applications to biology.
“High-throughput genomics has profoundly transformed the field of modern biology and has made it possible for scientists to make rapid scientific advances,” says the book’s co-author Dr. Raul Rabadan, professor of systems biology and founding director of Columbia University’s Program for Mathematical Genomics. “The explosion of data has hit biology, and as a result, we need new, more innovative analytical and computational tools to make sense of it all.”
Co-authored with Andrew J. Blumberg, PhD, professor of mathematics at University of Texas at Austin, the new book discusses techniques of topological data analysis, a rapidly developing subfield of mathematics that provides a methodology for analyzing the shape of data sets. The book offers several examples of these techniques and their use in multiple areas of biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes.
Drs. Rabadan and Blumberg are both prominent mathematicians with expertise in algebraic topology, a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. At Columbia, Dr. Rabadan also directs the Center for Topology of Cancer Evolution and Heterogeneity, of which Dr. Blumberg is a member. Dr. Rabadan’s work is mainly focused on developing tools to analyze genomic data, extracting the relevant information to understand the molecular biology, population genetics, evolution, and epidemiology of cancer. Dr. Blumberg, who also is a member of Columbia's Irving Institute for Cancer Dynamics, specializes in mathematics and computer science, with a particular interest in the applications of geometry and topology to the analysis of genomic data.