Feature Selection and Feature Extraction on Omics Data
by Saurav Mallik;Zhongming Zhao;Soumita Seth;Aimin Li;Kasmika Borah;Himanish Shekhar Das;
English | 2026 | ISBN: 1032967676 | 281 pages | True PDF EPUB | 30.07 MB
In today's data-driven world, biology and medicine are being transformed by the power of big data. Making sense of large, complicated biological datasets is a crucial problem that underlies every medical advancement and gene discovery. Feature Selection and Feature Extraction on Omics Data provides insight into this innovative area where biological science and computational science collide. This book, which is written in an approachable manner, explains the methods researchers employ to sort through vast amounts of multi-omics data to find insights that may result in better treatments, early disease diagnosis, and a greater comprehension of life at the molecular level. This volume provides a unique look at the technologies influencing the future of biological discovery and customized medicine, making it the perfect choice for anyone interested in learning more about how AI and data science are transforming biology and health. This collection explores cutting-edge feature selection and extraction methods across a broad range of omics data formats, such as metagenomics, genomics, transcriptomics, epigenomics, and datasets. Readers will learn how these techniques can be used to improve disease classification, find promising biomarkers, uncover significant biological patterns, and aid in early diagnosis. The chapters discuss techniques designed to regulate sparsity, minimize dimensionality, and preserve biological interpretability while fusing fundamental ideas with practical applications. Case studies and real-world applications show how these methods enhance computational models' performance in tasks like disease prediction and gene identification. This book is a great resource whether you're new to omics data analysis or looking to improve your current workflows using sophisticated feature engineering techniques. It connects theory and application with contributions from subject matter experts to assist readers in converting unprocessed data into biologically significant insights, making it an essential resource in contemporary computational biology and precision medicine. This book offers a comprehensive exploration of cutting-edge methodologies designed to address the complexities of high-dimensional biological datasets. This book serves as a practical and theoretical guide for researchers, data scientists, and students working at the intersection of bioinformatics and machine learning. This book is a comprehensive and application-focused approach to one of the most pressing challenges in modern bioinformatics: making sense of high-dimensional omics data. While many resources touch on machine learning or biological datasets in isolation, this book bridges the two, offering a unified, practical guide that combines theoretical depth with real-world implementation across diverse omics domains-including genomics, metagenomics, transcriptomics, and epigenomics data. iiKey features to emphasize in promotion: Interdisciplinary scope: Integrates bioinformatics, machine learning, and biological interpretation, making it valuable across disciplines including computational biology, genomics, and biomedical research. Practical focus: Demonstrates advanced techniques like feature selection and extraction in real omics contexts-gene, disease classification, and biomarker discovery. Emerging relevance: Addresses current trends in precision medicine, personalized healthcare, and AI-driven biology areas gaining major academic and industry interest. Wide accessibility: Suitable for researchers, students, and professionals at varying levels of expertise, with a balanced mix of conceptual background and applied examples. Fills a content gap: While feature engineering is well-studied in general AI, this book tailors it specifically to omics data, offering tools and strategies not commonly compiled in a single volume.
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