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

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.

Preface

- Pp. i-iii (3)
Roohi Sille, Tanupriya Choudhury, Sonal Talreja, S. Balamurugan
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Acknowledgements

- Pp. iv

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List of Contributors

- Pp. v-vi (2)

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Overview of Next Generation Genomic Science and Technology Utilising Machine Learning in Healthcare

- Pp. 1-20 (20)
Garima Sharma*

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A Review of AI-Driven Genomic Approaches for Cancer Detection and Personalised Medicine

- Pp. 21-60 (40)
P. S. Akshatha*, Gutta Geethika, Raghavi D. Kumar, Ramesh Prasad, Sonia D’souza, S. M. Dilip Kumar, G. Pius Agbulu

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Machine Learning Approaches for Microbiome Analysis and Applications

- Pp. 61-75 (15)
Aditya Vardhan*, Amarjeet Singh Chauhan, Sanjay Saini, Sagar Sharma

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Machine Learning Model for Annotation and Assembly of Genomes

- Pp. 76-93 (18)
Aditya Vardhan*, Amarjeet Singh Chauhan, Sanjay Saini, Sagar Sharma

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Extending Bayesian Classification to Predict Phase Transition in Biopolymer

- Pp. 94-113 (20)
Charu Kathuria, Deepti Mehrotra*, Navnit Kumar Misra

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Ethical Aspects of Analysing Genomic Data for Medical Analysis: A Comparative Study with a Focus on India

- Pp. 114-137 (24)
Ankit Vishnoi, Luxmi Sapra, Varun Sapra*, Parul Madan

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Integrating Pharmacogenomics and Machine Learning in Personalised Treatment Strategies for Parkinson’s Disease

- Pp. 138-160 (23)
Prachi Mall, Deepika Raina*

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Analysing Microbiome Metagenomics to Detect Anomaly using Variational Autoencoders (VAE) and Generative Adversarial Network (GAN) for Predicting Crohn’s Disease

- Pp. 161-178 (18)
Samrath Prakash*, Samir Jaiswal, Nikita Kukreti, Akhilesh Singh, Roohi Sille

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Leveraging ML Models for the Future of Genomes

- Pp. 179-195 (17)
Sumit Bhardwaj*

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Conclusion: The Transformative Role of Machine Learning in Genomic Science and Healthcare

- Pp. 196-209 (14)
Ankit Vishnoi*, Parul Madan, S. Balamurugan, Roohi Sille, Tanupriya Choudhury

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Subject Index

- Pp. 210-213 (4)
Roohi Sille, Tanupriya Choudhury, Sonal Talreja, S. Balamurugan
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