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Blockchain is a new type of technology that combines and secures information exchange between different stakeholders such as medical practitioners, patients, healthcare providers, and other applicable parties. Among them, Blockchain Technology is one of the most important areas in the bioinformatics application of biomedical research and healthcare systems utilizing unique requirements and integration features. All the chapters are written by experts and researchers working in various areas of the biomedical and healthcare domain and they also dive into one of the most overlooked methodological, practical, and moral questions to secure and handle the enormous amount of data being generated from IoT-enabled biomedical and healthcare systems. In the beginning, this book presents an overview and then discusses open issues, challenges, and applicability aspect of Blockchain technology in healthcare. Then, this book presents a variety of perspectives on the most pressing questions in the field, for example: how IoT can connect billions of biomedical and healthcare information; how the blockchain-based secure access control mechanisms in biomedical and healthcare work; how to address the Quality-of-Service (QoS) and real-time accessibility requirements for healthcare applications; and how to ensure communication with efficiency. Also, it discusses Blockchain for IoT-enabled healthcare systems and presents a comparative analysis with respect to various performance evaluation metrics too.

This book updates knowledge on recent advances in computational, biophysical and bioinformatics tools/techniques and their practical applications in modern drug design and discovery paradigm. It also encompasses fundamental principles, advanced methodologies and applications of various CADD approaches including several cutting-edge areas; presenting recent developments covering ongoing trends in the field of computer-aided drug discovery. Having contributions by a global team of experts, the book is expected to be an ideal resource for drug discovery scientists, medicinal chemists, pharmacologists, toxicologists, phytochemists, biochemists, biologists, R&D personnel, researchers, students, teachers and those working in the field of drug discovery. It will fill the knowledge gaps that exist in the current CADD approaches and methodologies/ protocols being widely used in both academic and research practices. Further, a special focus on current status of various computational drug design approaches (SBDD, LBDD, de novo drug design, pharmacophore-based search), bioinformatics tools and databases, computational screening and modeling of phytochemicals/natural products, artificial intelligence and machine learning, and network pharmacology and systems biology would certainly guide researchers, students or readers to conduct their research in the emerging area(s) of interest. It is also expected to be highly beneficial to various stakeholders working in the pharmaceutical and biotechnology industries (R&D), the academic as well as research sectors.

This book provides the most valuable and updated research on computational and mathematical models in biological systems from influential researchers around the world and contributes to the development of future research guidelines in this topic. Topics include (but are not limited to): modeling infectious and dynamic diseases; regulation of cell function; biological pattern formation; biological networks; tumor growth and angiogenesis; complex biological systems; Monte Carlo methods; Control theory, optimization and their applications

The textbook is designed to provide a'non-intimidating'entry to the field of mathematical biology. It is also useful for those wishing to teach an introductory course. Although there are many good mathematical biology texts available, most books are too advanced mathematically for most biology majors. Unlike undergraduate math majors, most biology major students possess a limited math background. Given that computational biology is a rapidly expanding field, more students should be encouraged to familiarize themselves with this powerful approach to understand complex biological phenomena. Ultimately, our goal with this undergraduate textbook is to provide an introduction to the interdisciplinary field of mathematical biology in a way that does not overly terrify an undergraduate biology major, thereby fostering a greater appreciation for the role of mathematics in biology

This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.

As a component of post-genome science, the field of proteomics has assumed great prominence in recent years. Whereas quantitative analyses focussed initially on relative quantification, a greater emphasis is now placed on absolute quantification and consideration of proteome dynamics. Coverage of the topic of quantitative proteomics requires consideration both of the analytical fundamentals of quantitative mass spectrometry and the specific demands of the problem being addressed. Quantitative Proteomics aims to outline the state of the art in mass spectrometry-based quantitative proteomics, describing recent advances and current limitations in the instrumentation used, together with the various methods employed for generating high quality data. Details on both strategies describing how stable isotope labelling can be applied and methods for performing quantitative analysis of proteins in a label-free manner are given. The utility of these strategies to understanding cellular protein dynamics are then exemplified with chapters looking at spatial proteomics, dynamics of protein function as determined by quantifying changes in protein post-translational modification and protein turnover. Finally, a key application of these techniques to biomarker discovery and validation is presented, together with the rapidly developing area of quantitative analysis of protein-based foodstuffs. This exemplary book is essential reading for analytical and biological mass spectrometrists working in proteomics research, as well as those undertaking either fundamental or clinical-based investigations with an interest in understanding protein dynamics and/or biomarker assessment.

Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. The field continues to develop intensively in both academia and commercially, and is highly interdisciplinary. This broad-ranging and thoroughly updated second edition covers new findings while retaining the successful formula of the original text. Bioinformatics: An Introduction (2nd Edition) is structured into three parts devoted to Information, Biology, and Applications. Every section of the book has been completely revised for currency, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of key concepts. Furthermore, two new chapters provide instruction about algorithms and knowledge representation. Emphases are placed on the underlying fundamentals and on acquisition of a broad and comprehensive grasp of the field as a whole. Features: • Provides a solid foundation in, and self-contained introduction to, the field of bioinformatics and its state-of-the-art as it relates to computational biology research • Offers additional chapters on algorithms and knowledge representation, including text mining [NEW] • Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context • Contains a greatly expanded chapter on interactions and regulatory networks [NEW] • Includes examples, definitions, problems and a comprehensive and useful bibliography • Presents new experimental methods, and serves as a springboard for new research [NEW] • Incorporates discussion of the method of drawing inferences from abstract sequence analysis based on frequency dictionaries • Contains an extensively revised chapter on medical applications[NEW] • Emphasizes the underlying fundamentals and acquisition of a broad and comprehensive grasp of the field as a whole This significantly improved second edition of a successful textbook is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, thereby allowing the reader to gain much insight into the state-of-the-art of bioinformatics.

A shift in culture Only a decade ago, the?rst thing a molecular biologist would have had to learn when he or she started the lab work was how to handle pipettes, extract DNA, useenzymesandcloneagene. Now,the?rstthing thatheorsheshould learn is how to handle databases and to extract all the information that is already known about the gene that he or she wants to study. In all likelihood, he or she will?nd that the gene has already been sequenced from several organisms, that it was recovered in a variety of EST projects, that expression data are available from microarray and SAGE studies, that it was included in linkage studies, that proteomics data are rapidly accumulating, that lists of interacting proteins are being compiled, that domain structure data are available and that it is part of a network of genetic interactions which is intensively modelled. He or she will discover that all this information resides in many di?erent databases with di?erent data formats and with di?erent levels of analyses and linking. Starting to work on this gene will make sense only, if all this information is put together in a project-speci?c manner and set into the context of what is known about related genes and processes. At this point he or she may decide to walk up to the bioinformatics group in house and ask for help with arranging the data in a useful manner.