Feature Selection and Feature Extraction on Omics Data

Saurav Mallik, Zhongming Zhao, Soumita Seth, Aimin Li, Kasmika Borah, Himanish Shekhar Das

• Publisher: Chapman and Hall/CRC
• Year: 2026
• eBook ISBN: 9781040674055
• Topic: Medicine
• Subtopic: Neural Networks

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.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Contents
• About the Editors
• List of Contributors
• Preface
• Chapter 1 Machine Learning and Statistic-Based Feature Selection and Extraction Approach for Omics Data
• Chapter 2 Advanced Feature Selection and Extraction Techniques for Omics Data Analysis: Applications in Multi-Omics Integration
• Chapter 3 Role of Bioinformatics and Feature Selection Approaches in Analyzing Metagenomics Data
• Chapter 4 Feature Extraction and Selection Methods and Bioinformatics on Omics Data to Identify Signatures for Schizophrenia Mental Health Disorder
• Chapter 5 Feature Selection through Brownian Motion Search: A Case Study for Breast Cancer Prediction
• Chapter 6 Feature Extraction and Selection Methods and Bioinformatics Approach on Omics Data to Identify Molecular Signatures for Specific Diseases
• Chapter 7 Feature Extraction and Selection Methods Outperform Machine Learning and Deep Learning Techniques
• Chapter 8 Combining Randomized Tree and Cuckoo Search for Cancer Prediction: A Case Study of Hybrid Feature Selection
• Chapter 9 Complexity to Clarity: Feature Selection and Extraction in Plant and Microbial From Omics Research
• Chapter 10 Analysis of Skin Diseases Using Deep Learning Techniques
• Index