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Books in Bioinformatics and computational biology

BooksJournals

    • 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.

    • The Three Functional States of Proteins

      Structured, Intrinsically Disordered, and Phase Separated
      • 1st Edition
      • Timir Tripathi + 1 more
      • English
      The Three Functional States of Proteins explores how structured proteins, intrinsically disordered proteins, and phase separated proteins contribute to the complexity of cellular life, and offers insights into their roles in both health and disease. It discusses the latest research findings and highlight groundbreaking discoveries and innovative methodologies used to study these protein states.Traditionally... the different states of proteins have been defined based on their structures and functions. However, it is becoming increasingly clear that these criteria alone may not be sufficient to capture the complex and multifaceted properties of these molecules. Definitions based on thermodynamics and kinetics are now recognized as potentially more appropriate for comprehensively understanding protein states. Emerging evidence indicates that under physiological conditions, a majority of proteins possess the capability to exist in and transition between the native, droplet, and amyloid states. These distinct states play crucial roles in various cellular functions, influenced significantly by their physicochemical and structural properties. The book also considers the interactions among these states and discusses how their internal organization as individual molecules, as well as their collective organization as molecular assemblies are stabilized. Furthermore, it examines the processes by which these states are formed and the cellular functions associated with each specific state.

    • Mining Biomedical Text, Images and Visual Features for Information Retrieval

      • 1st Edition
      • Sujata Dash + 3 more
      • English
      Mining Biomedical Text, Images and Visual Features for Information Retrieval provides broad coverage of the concepts, themes, and instrumentalities of the important, evolving area of biomedical text, images, and visual features towards information retrieval. The book aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research. Topics covered include Internet of Things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications. This is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work.

    • Machine Learning Models and Architectures for Biomedical Signal Processing

      • 1st Edition
      • Suman Lata Tripathi + 3 more
      • English
      Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.

    • Next Generation eHealth

      Applied Data Science, Machine Learning and Extreme Computational Intelligence
      • 1st Edition
      • Miltiadis Lytras + 3 more
      • English
      Next Generation eHealth: Applied Data Science, Machine Learning and Extreme Computational Intelligence discusses the emergence, the impact, and the potential of sophisticated computational capabilities in healthcare. This book provides useful therapeutic targets to improve diagnosis, therapies, and prognosis of diseases, as well as helping with the establishment of better and more efficient next-generation medicine and medical systems. Machine learning as a field greatly contributes to next-generation medical research with the goal of improving medicine practices and medical Systems. As a contributing factor to better health outcomes, this book highlights the need for advanced training of professionals from various health areas, clinicians, educators, and social professionals who deal with patients. Content illustrates current issues and future promises as they pertain to all stakeholders, including informaticians, professionals in diagnostics, key industry experts in biotech, pharma, administrators, clinicians, patients, educators, students, health professionals, social scientists and legislators, health providers, advocacy groups, and more. With a focus on machine learning, deep learning, and neural networks, this volume communicates in an integrated, fresh, and novel way the impact of data science and computational intelligence to diverse audiences.