Predictive Methods for Genomics and Evolution

Innovative title providing a systematic account of new predictive alignment-free methods in genomics and bioinformatics, emphasizing their computational efficiency

Predictive Methods for Genomics and Evolution provides a cohesive overview of major alignment-based and alignment-free methods in genomics and bioinformatics, primarily based on DNA/RNA. Throughout the book, contrasts between current conventional methods and novel alignment-free methods are presented and evaluated across a wide range of topics.

Written by a team of experienced academics with significant research experience in the field, Predictive Methods for Genomics and Evolution discusses sample topics including:

• Major unresolved problems in biology including the most fundamental concept of species, the nature of evolution and speciation, phylogenetic inference, pathogenicity, and the origin of life
• Limitations of alignment-based predictive technologies such as high resource usage
• Novel interpretations of current hypotheses from a biological perspective with wide-ranging applications in bioinformatics and medicine
• Insights on the shift in the research status quo towards a wider application of alignment-free methodologies, fueled by increased availability of data and advances in science and technology

Predictive Methods for Genomics and Evolution is an essential guide on the subject for professionals, academics, researchers, and students within the fields of genomics, evolutionary biology, phylogenetics and taxonomy, and computational biology and bioinformatics, as well as medical practitioners in related fields.

Contents

Chapter 1 What is Biology?

Chapter 2 Genomes to Phenotypes, Environments, and Genomes

Chapter 3 Species Definitions and their Limitations

Chapter 4 Speciation and Evolution

Chapter 5 From Genomes to Evolution

Chapter 6 Conventional Phylogenetics and Systematics

Chapter 7 New Approaches to Phylogenetics

Chapter 8 About the Origin of Life

Chapter 9 Towards an Analytical Biology

Chapter 10 Appendices

Max Garzon is Professor of computer science and bioinformatics at the University of Memphis, where he teaches both undergraduate and graduate courses in data science, neurocomputing, bioinformatics, foundations of computing and cyber ethics.

Luis Fernando Garcia is an Associate Professor at the National University of Colombia.

Fredy Alexander Colorado is a researcher who has spent doctoral stays at Clemson University, USA and the University of Alberta, Canada.