Predictive Methods for Genomics and Evolution. Towards a New Analytical Biology

Tabla de contenidos

• 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

Descripción

Predictive Methods for Genomics and Evolution offers a systematic account of novel alignment-free methods in genomics and bioinformatics, emphasizing their potential to add predictive capabilities to address major and current questions in biological science. Presenting a cohesive comparison of alignment-based and alignment-free methodologies, mainly focused on DNA/RNA, the book evaluates and contrasts both approaches across a wide range of applications.

Written by experienced academics with extensive research backgrounds in the field, this resource explores pivotal biological problems and emerging computational strategies that are shaping modern genomics.

Predictive Methods for Genomics and Evolution discusses major topics including:

• Fundamental unresolved biological questions: species concept, evolution and speciation, phylogenetic inference, pathogenicity, and the origin of life
• Novel interpretations of current hypotheses with wide-reaching applications in bioinformatics and medicine
• The shift toward more efficient alignment-free methodologies supported by growing data availability, deeper understanding of DNA/RNA, and advancements in machine learning and data science

Predictive Methods for Genomics and Evolution is an essential guide for professionals, academics, researchers, and students in genomics, evolutionary biology, phylogenetics, taxonomy, computational biology, and bioinformatics, as well as medical practitioners working with genomic data.

A companion website for this text can be accessed at: bmc.memphis.edu/predBiology