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Elsevier

Books in Bioinformatics and computational biology

  • Human-AI-Cognitive Intelligence Software, System, and Services for Quality Assurance

    Theories and Methods with Systems Thinking for Behaviour Change in the Medical Decision-Making Pathway
    • 1st Edition
    • Pantea Keikhosrokiani
    • English
    Habit-based Behavior Change Medical Information Support System and Artificial Intelligence: Theories, Methods, and Data Analytics Approach provides a guideline to design and implement Habit-based Behavior Change Support Systems (HBCSS) which can change patient’s unhealthy habits to prevent the development of diseases. It presents theories, methods, management, and data analytics approach required to design, implement, and prescribe the use of HBCSS for several diseases’ management.It discusses topics such as theories of behavior change, ontologies and knowledge management, data mining, privacy and security, descriptive and prescription analytics. In addition, it discusses how to measure habit-change, future directions of the field, and case studies based on real-world examples.It is a valuable resource for clinicians, researchers, students, and member of the biomedical and medical fields who want to learn more about the use of medical systems to improve patients’ health.

  • Artificial Intelligence Based Primary Care

    Artificial Intelligence and Human Cognition in General Practice and Family Medicine
    • 1st Edition
    • Anthony C. Chang + 5 more
    • English
    Artificial Intelligence Based Primary Care: Artificial Intelligence and Human Cognition in General Practice and Family Medicine provides a comprehensive look into the integration of artificial intelligence within primary care. This book aims to bridge the gap between data scientists and primary care clinicians by presenting potential AI applications, implementation challenges, and real-world clinical scenarios in an accessible manner. Comprised of 5 sections and 20 chapters, it offers both theoretical insights and practical guidance for family medicine practitioners.Additi... the book includes a chapter dedicated to a day in the life of a General Practitioner, showcasing the practical impact of AI. It also features an extra compendium listing the Top 100 articles and best books on Artificial Intelligence. This resource is invaluable for academics, researchers, and clinicians eager to expand their knowledge in this emerging field.

  • Advances in Structural Biology

    Applications in Protein Structure, Function, and Disease
    • 1st Edition
    • Anas Shamsi + 3 more
    • English
    Advances in Structural Biology: From Protein Structures to Function and Disease offers a comprehensive, practical resource that bridges the latest experimental and computational approaches in structural biology. Backed by real-world case studies, the book demonstrates how these technologies can be utilized to serve a range of biological needs. The content spans foundational principles of protein structure and bioinformatics tools, cutting-edge methodologies such as MicroED and Cryo-EM, and the transformative role of artificial intelligence and big data analytics in structural biology. It also considers applications including drug discovery, vaccine development, antiviral therapy, personalized medicine, and the structural basis of aging and neurodegenerative diseases.Readers will find detailed investigation into the latest developments in structural and computational biology, including insights into integrative methodologies that combine both traditional and emerging technologies. This resource is invaluable for advanced students, researchers, and professionals aiming to harness structural biology for innovative solutions in precision medicine and translational research.

  • Statistical Bioinformatics with R

    • 2nd Edition
    • Sunil K. Mathur
    • English
    Statistical Bioinformatics with R, Second Edition offers a balanced treatment of statistical theory within the context of bioinformatics applications. The book goes beyond gene expression and sequence analysis to include a careful integration of statistical theory in bioinformatics. The inclusion of R codes, along with the development of advanced methodologies such as Bayesian and Markov models, equips students with a solid foundation for conducting bioinformatics research. Sections incorporate the latest advancements in bioinformatics and statistical methodologies, including new chapters on cutting-edge topics such as high-throughput sequencing data analysis, AI/machine learning applications in bioinformatics, and advanced statistical methods.From new and updated practical examples and case studies that illustrate real-world applications of statistical techniques to bioinformatic problems to enhanced end-of-chapter exercises, detailed code annotations, and an improved companion website with supplementary materials, including datasets and R scripts, this book is a valuable resource for both self-study and formal coursework, fostering a deeper understanding of statistical bioinformatics and equipping readers with the skills needed to tackle complex biological data analysis challenges.

  • Genome Analysis

    Principles and Methods
    • 1st Edition
    • Dev Bukhsh Singh + 1 more
    • English
    Genome Analysis: Principles and Methods provides recent and advanced information about genome analysis approaches and techniques to study and annotate the structure and function of the genome. It is a compendium of important topics such as NGS analysis, genome fragmentation and assembly, metagenomics, cloning and expression, physical marker analysis, transcriptome data analysis, sequence alignment and comparison, evolutionary analysis, SNP analysis, genome-based disease diagnosis and therapies, micro-RNAs, pharmacogenomics, genetic approaches to disease intervention, and challenges with opportunities in genome analysis and genomics, etc.The latest developments in the field are discussed, and key concepts are introduced to ensure readers understand advanced concepts and methodologies in the area. The book serves as a valuable guide to the present, emerging, and evolving research methodologies in the field.

  • Digital Health Maturity: Quality, Interoperability, and Innovation

    • 1st Edition
    • Siaw-Teng Liaw + 3 more
    • English
    Digital Health Maturity: Quality, Interoperability, and Innovation provides a roadmap to move from endless pilots and ad hoc system purchase to a systematic, stepwise, and integrated approach to increasing digital health capacity. Specific guidelines, tools, and use cases are discussed to show how the digital health maturity metamodel (DHM3) can be put into actual practice. The book discusses topics such as foundations of digital health and how to put them into practice; organizational considerations for implementation; and best practices, tools, and pitfalls to avoid. In addition, it discusses the future of digital transformation and the impact of a global adherence to digital health. It is a valuable resource for researchers, students, policy makers, governments and anyone who is interested in learning more about digital health and its benefits worldwide.

  • Healthcare Applications of Neuro-Symbolic Artificial Intelligence

    • 1st Edition
    • Boris Galitsky
    • English
    Healthcare Applications of Neuro-Symbolic Artificial Intelligence provides a comprehensive introduction to the field of neuro-symbolic (NS) artificial intelligence (AI), presenting the most recent advances in deep learning and integration of NS systems and large language models (LLMs). This book evaluates traditional approaches, current approaches, as well as the author’s own approach to NS, to create hybrid architectures and reasoning techniques to overcome the limitations of most existing AI systems such as deep learning, neural networks, and symbolic AI.This book will be a welcome resource for researchers and graduate students in AI, natural language processing, and biomedical informatics, as well as professionals in software development looking to redesign current systems to leverage LLMs through the health application of NS architecture.

  • Multi-Omics Technology in Human Health and Diseases

    Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omics
    • 1st Edition
    • Muzafar A Macha + 2 more
    • English
    Multi-Omics Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omic offers an advanced exploration into the comprehensive understanding of disease etiology and prognosis through multiomics approaches. This authoritative volume delves into the applications of multiomics technology in elucidating complex human health conditions and diseases. It introduces the technology's potential for biomarker identification, drug discovery, and disease prognostication. For a thorough understanding of human health and diseases, particularly cancer, it is essential to integrate knowledge of molecular biomarkers across multiple omics levels, including the genome, epigenome, transcriptome, proteome, and metabolome. This resource addresses the current gaps in knowledge among students and researchers, providing in-depth coverage of multiomics technology and its implementation in scientific research and discovery.Multi-Omic... Technology in Human Health and Diseases: Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Radiomics, Multi-omics is a pioneering resource that presents cutting-edge information on contemporary multiomics technologies for big data interpretation and their applications in deciphering complex human pathobiology. This comprehensive guide is indispensable for researchers, academics, students, and industry professionals alike.

  • Deep Learning in Genetics and Genomics

    Volume 1: Foundations and Introductory Applications
    • 1st Edition
    • Khalid Raza
    • English
    Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications, the intersection of deep learning and genetics opens up new avenues for advancing our understanding of the genetic code, gene regulation, and the broader genomics landscape. The book not only covers the most up-to-date advancements in the field of deep learning in genetics and genomics, but also a wide spectrum of (sub) topics including medical and clinical genetics, predictive medicine, transcriptomic, and gene expression studies. In 21 chapters Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications describes how AI and DL have become increasingly useful in genetics and genomics research where both play a crucial role by accelerating research, improving the understanding of the human genome, and enabling personalized healthcare. From the fundamentals concepts and practical applications of deep learning algorithms to a wide range of challenging problems from genetics and genomics, Deep Learning in Genetics and Genomics vol. 1, Foundations and Applications creates a better knowledge of the biological and genetics mechanisms behind disease illnesses and improves the forecasting abilities using the different methodologies described. This title offers a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning-based approaches in genetics and genomics, helping researchers process and interpret vast amounts of genetic data, identify patterns, and make discoveries that would be challenging or impossible using traditional methods.

  • Deep Learning in Genetics and Genomics

    Volume 2: Advanced Applications
    • 1st Edition
    • Khalid Raza
    • English
    Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences.