Feature Selection and Feature Extraction on Omics Data

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.

Saurav Mallik is currently working as Research Scientist in the Department of Pharmacology and Toxicology, The University of Arizona, USA. Previously, he worked as Postdoctoral Fellow in Harvard T.H. Chan School of Public Health, Boston, MA, USA for more than three years (2019-2022), the Center of Precision Health, Department of School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA for one and half year (2018-2019), and in the Division of Bio-statistics, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA for more than one year (2017-2018). He obtained his PhD degree in the Department of Computer Science and Engineering (C.S.E.) from Jadavpur University, Kolkata, India in 2017 while his PhD works carried out in Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India as Junior Research Fellow and Visiting Scientist. He has obtained the award of Research Associateship from CSIR (Council of Scientific and Industrial Research), MHRD, Govt. of India in 2017. Dr. Mallik has more than 150 research papers in different top high impact factor peer-reviewed International Journals, Conferences and Book Chapters. He published several Books and Patents. He is working as the active member of Institute of Electrical and Electronics Engineers (IEEE), USA, ACM and American Association for Cancer Research (AACR), USA and Bioclues, India. He has also worked with section editors and reviewers with several well-reputed high impact journals. His research interest includes Computational Biology, Knowledge Retrieval and Data Mining, Bioinformatics, Bio-Statistics and Machine Learning/Deep Learning.

Zhongming Zhao, Ph.D., M.S. is a chair professor at McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston (UTHealth). He holds University Chair for Precision Health and is the founding director of the Center for Precision Health. Before he joined UTHealth in 2016, he was Ingram Endowed Professor of Cancer Research, Professor (tenured) in the Departments of Biomedical Informatics, Psychiatry, and Cancer Biology at Vanderbilt University Medical Center, Chief Bioinformatics Officer of the Vanderbilt-Ingram Cancer Center (VICC), and Director of the VICC Bioinformatics Resource Center. Dr. Zhao has broad interests in bioinformatics, genomics, population genetics, precision medicine, and machine learning and has co-authored over 500 total publications in these areas (cited by >25,000 times, H-index = 80). Dr. Zhao has served as the Editor-in-Chief, Associate Editor, or editorial board member of 22 journals. Dr. Zhao is the founding president of The International Association for Intelligent Biology and Medicine (IAIBM, 2018). He was elected as a fellow in the American College of Medical Informatics (ACMI, 2021), the American Medical Informatics Association (FAMIA, 2022), and the American Institute for Medical and Biological Engineering (AIMBE, 2023).

Soumita Seth is currently serving as an Assistant Professor in the Department of Computer Science and Engineering of Future Institute of Engineering and Management, Kolkata, India, Affiliated to MAKAUT, Kolkata. Besides, she is recently submitted her PhD thesis in the Department of Computer Science & Engineering (CSE) from a state-government university, Aliah University (AU), Kolkata, India. Previously, She completed M.Tech. and B.Tech. from the departments of CSE and IT, respectively. She is also collaborating her PhD research with The University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA. She has Academic Experience of almost 7 years, Fulltime Research Experience of 2 years, and Industrial Experience of 2 years. Dr. Seth has more than 10 research papers in different top high impact factor peer-reviewed International Journals, Conferences and Book Chapters. She is also publishing forthcoming books with Scopus indexed publishers (Online link is also available). She has also worked with section editors and section reviewers with several well-reputed high impact journals. Her research interests include Computational Biology, Data Mining, Bioinformatics, Pattern Recognition, Biological Regulatory Networks, Biostatistics, Machine learning/Deep learning.

Aimin Li is an assistant professor of Xi'an University of Technology, China. He got his master degree from Xi'an University of Technology, and doctoral degree from Xidian University. He previously worked as a visiting scientist in University of Texas Health Science Center, Houston, Texas, USA. His current research applications are in the areas of machine learning, bioinformatics, and regulatory networks. He has published 20+ research papers. He is also an editor of International Journal of Computational Biology and Drug Design, PC member of ICIBM (International Conference on Intelligent Biology and Medicine), and co-chair of BIBM IWRI 2020.

Kasmika Borah is a researcher in the department of Computer Science and IT, Cotton University, Assam India. She completed her masters in Bioinformatics from Dibrugarh University, Assam, India. She has over five years of experience doing research in the fields of drug design, machine learning of next-generation sequencing data, network pharmacology, molecular dynamic simulation, drug repurposing and medical dataset annotation, image processing. She worked on the startup company of AI-based software development. She worked as a Project JRF at CSIR-NEIST, India. She published five articles in reputed international journals and one book chapter in Springer.

Himanish Shekhar Das received the B.Tech degree in Computer Science and Engineering from the Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India, in 2012, the M.Tech. degree from the Department of Computer Science and Engineering, National Institute of Technology Durgapur, Assam, India, in 2015, and the Ph.D. degree from the Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India, in 2021. He is currently working as an Assistanr Professor with the Department of Computer Science and Information Technology, Cotton University, Assam, India. Before joining Cotton University, he worked as an Assistant Professor with the Department of Computer Science and Engineering, Jorhat Engineering College, Assam, India and Birla Institute of Technology Mesra, Ranchi, India. He has published more than 30 papers in international/national journals/conference proceedings, and book chapters. His research interests include Speech Processing (Language Identification); Medical Image Processing; Machine Learning; Deep Learning; Bioinformatics.