Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry, VOL 4 | 13.14 MB
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
Author: Roohi Sille, Tanupriya Choudhury, Sonal Talreja & S. Balamurugan
Category: Nonfiction, Computers, Advanced Computing, Artificial Intelligence
Language: English | 232 Pages | ISBN: 9798898815493
Description:
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
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
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