Data Science for Genomics 1st Edition

Capture the future of life sciences with Data Science for Genomics, 1st Edition by Amit Kumar Tyagi and Ajith Abraham. This authoritative guide immediately draws you into the vital intersection of data science and genomics, making complex concepts accessible for researchers, students, and healthcare professionals worldwide.

Discover a focused, practical approach that connects theory to real-world genomic challenges. The book explains core techniques—statistical modeling, machine learning and deep learning algorithms, data preprocessing, and visualization—applied to next‑generation sequencing (NGS), gene expression, variant analysis, and biomarker discovery. Clear examples, step‑by‑step workflows, and case studies illuminate how to build reproducible genomic pipelines and derive actionable insights from large-scale biological data.

Designed for both newcomers and experienced practitioners, the text balances mathematical rigor with applied guidance. Learn how to handle noisy datasets, select features for predictive models, interpret results for precision medicine, and integrate heterogeneous data sources. The authors emphasize best practices in data quality, validation, and ethical considerations relevant to genomics research and clinical applications.

Whether you’re a bioinformatician, data scientist, molecular biologist, or graduate student, this volume equips you with the tools to transform genomic data into discovery. It’s a practical reference for university courses, laboratory teams, and industry professionals working in personalized medicine, drug discovery, and population genomics.

Bring clarity to your genomic projects and accelerate your research impact—order your copy of Data Science for Genomics, 1st Edition by Amit Kumar Tyagi and Ajith Abraham today and start turning genomic data into meaningful outcomes.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.