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Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry
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Editors: Sonal Talreja, Tanupriya Choudhury, Roohi Sille, S. Balamurugan

Series Title: Applied Machine Learning for IoT and Data Analytics (volume 4)

Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry

Volume 4
eBook: US $69 Special Offer (PDF + Printed Copy): US $125
Printed Copy: US $90
Library License: US $276
ISBN: 979-8-89881-550-9 (Print)
ISBN: 979-8-89881-549-3 (Online)
Year of Publication: 2026
DOI: 10.2174/97988988154931260401
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Introduction

Applied Machine Learning for IoT and Data Analytics (Volume-4)- Elevating Next Generation Genomic Science and Technology Using Machine Learning for the Healthcare Sector explores how machine learning and artificial intelligence are transforming modern genomics and healthcare. The book highlights how advanced computational methods can analyse complex genomic data to improve disease detection, enable personalised treatments, and support precision medicine.

It covers key applications such as AI-driven cancer detection, microbiome analysis, genome annotation, mutation detection, and pharmacogenomics. The chapters also discuss real-world healthcare applications, ethical concerns, and data privacy challenges, offering a balanced view of both opportunities and limitations in AI-driven genomic research.


Key Features

  • - Comprehensive coverage of machine learning applications in genomics.
  • - Focus on precision medicine, disease prediction, and personalised therapies.
  • - Integration of bioinformatics, AI, and healthcare applications.
  • - Real-world case studies and analytical frameworks.
  • - Discussion of ethical, privacy, and regulatory considerations.

Target Readership :

Researchers, academics, students and professionals interested in AI-driven healthcare and precision medicine.


eBook: US $69 Special Offer: US $125 Printed Copy: US $90 Library License: US $276

Foreword

The primary purpose of this book is to:

• Discover New Insights: As is known, Machine learning techniques can uncover hidden patterns and relationships within genomic data that may not be apparent through traditional analysis methods, leading to new discoveries and insights into genetic mechanisms underlying diseases and traits. Collecting them in a book facilitates ease of access to the current research and advancements in the field.

• Facilitate Comparative Genomics Research: All articles and studies presented together can aid in comparative genomics studies by efficiently comparing genomic data across the diversity of different species or individuals, elucidating evolutionary relationships and identifying conserved genomic elements.

• Overall, this book offers the potential to revolutionise genomic research by presenting vast amounts of genomic information generated through advances in machine learning technologies. This can help in advancing our understanding of biology and improving human health.

This preface sets the stage for a comprehensive exploration of the revolutionary capabilities offered by the field of Machine learning, making it a powerful tool in genomic mapping.

Sonal Talreja
School of Computer Science
University of Petroleum and Energy Studies (UPES)
Bidholi Campus, via Prem Nagar
Uttarakhand, 248007
India