Topological Data Analysis For Genomics And Evolution

Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.

An introduction to geometric and topological methods to analyze large scale biological data; includes statistics and genomic applications.

This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.

This timely text introduces topological data analysis from scratch, with detailed case studies.

It will be an invaluable tool for anyone interested or working in molecular or evolutionary biology, and for those studying the implications of our rapidly increasing knowledge of the genomes of organisms."--BOOK JACKET.

This book constitutes the refereed proceedings of the 20th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2024, which was held in Bhubaneswar, India, during January 17–20, 2024. The 24 full papers presented in this volume were carefully reviewed and selected from 116 submissions. The papers are organized in the following topical sections: Distributed Computing (DC) and Intelligent Technology (IT). The DC track solicits original research papers contributing to the foundations and applications of distributed computing, whereas the IT track solicits original research papers contributing to the foundations and applications of Intelligent Technology.

A concise and accessible guide to the coronavirus/COVID-19, fully updated with information on variants, treatment options and vaccines.

Summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution.