Translational Bioinformatics and Systems Biology Methods for Personalized Medicine introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine. Through the description of important cutting-edge technologies in bioinformatics and systems biology, readers may gain an essential understanding of state-of-the-art methodologies. The book discusses topics such as the challenges and tasks in translational bioinformatics; pharmacogenomics, systems biology, and personalized medicine; and the applicability of translational bioinformatics for biomarker discovery, epigenomics, and molecular dynamics. It also discusses data integration and mining, immunoinformatics, and neuroinformatics. With broad coverage of both basic scientific and clinical applications, this book is suitable for a wide range of readers who may not be scientists but who are also interested in the practice of personalized medicine. Introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine Presents a problem-solving oriented methodology to deal with practical problems in various applications Covers both basic scientific and clinical applications in order to enhance the collaboration between researchers and clinicians Brings integrative and multidisciplinary approaches to bridge the gaps among various knowledge domains in the field

Translational Bioinformatics is an emerging field in the direction of biomedical research. High throughput technologies can be applied to the generated biological data to develop the vaccine and personalized medicine. This volume consists of the chapters from different stalwart of the field covering the topics such as drug development, vector engineering, vaccine development and translational genomics. Chapters covered in this volume discuss the translational research related with cancer, Alzheimer disease and cardiovascular diseases. This volume includes the chapter describing the importance of computational resources and chemoinformatics for the translational health research. How Omics studies are helping to translate the laboratory data into the development of tools which are beneficial in the clinics have been described. How translational bioinformatics helpful in plant genomics to improve the crops have also been included in this volume. This volume has a chapter which describes the secrets of resistance development and further how these resistance are associated with human infectious diseases. This volume will be useful to the early career researcher in the development of research idea and develop their methodologies in the direction of bioinformatics and it will also give the insight to translate their findings. - Translational Bioinformatics - Health Informatics - Multi-omics

Translational Bioinformatics in Healthcare and Medicine offers an overview of main principles of bioinformatics, biological databases, clinical informatics, health informatics, viroinformatics and real-case applications of translational bioinformatics in healthcare. Written by experts from both technology and clinical sides, the content brings together essential knowledge to make the best of recent advancements of the field. The book discusses topics such as next generation sequence analysis, genomics in clinical care, IoT applications, blockchain technology, patient centered interoperability of EHR, health data mining, and translational bioinformatics methods for drug discovery and drug repurposing. In addition, it discusses the role of bioinformatics in cancer research and viroinformatics approaches to counter viral diseases through informatics. This is a valuable resource for bioinformaticians, clinicians, healthcare professionals, graduate students and several members of biomedical field who are interested in learning more about how bioinformatics can impact in their research and practice. - Covers recent advancements in translational bioinformatics and its healthcare applications - Discusses integrative and multidisciplinary approaches to U-healthcare systems development and management - Bridges the gap among various knowledge domains in the field, integrating both technological and clinical knowledge into practical content

This book offers a detailed overview of translational bioinformatics together with real-case applications. Translational bioinformatics integrates the areas of basic bioinformatics, clinical informatics, statistical genetics and informatics in order to further our understanding of the molecular basis of diseases. By analyzing voluminous amounts of molecular and clinical data, it also provides clinical information, which can then be applied. Filling the gap between clinic research and informatics, the book is a valuable resource for human geneticists, clinicians, health educators and policy makers, as well as graduate students majoring in biology, biostatistics, and bioinformatics.

Translational bioinformatics (TBI) involves development of storage, analytics, and advanced computational methods to harvest knowledge from voluminous biomedical and genomic data into 4P healthcare (proactive, predictive, preventive, and participatory). Translational Bioinformatics Applications in Healthcare offers a detailed overview on concepts of TBI, biological and clinical databases, clinical informatics, and pertinent real-case applications. It further illustrates recent advancements, tools, techniques, and applications of TBI in healthcare, including Internet of Things (IoT) potential, toxin databases, medical image analysis and telemedicine applications, analytics of COVID-19 CT images, viroinformatics and viral diseases, and COVID-19–related research. Covers recent technologies such as Blockchain, IoT, and Big data analytics in bioinformatics Presents the role of translational bioinformatic methods in the field of viroinformatics, as well as in drug development and repurposing Includes translational healthcare and NGS for clinical applications Illustrates translational medicine systems and their applications in better healthcare Explores medical image analysis with focus on CT images and novel coronavirus disease detection Aimed at researchers and graduate students in computational biology, data mining and knowledge discovery, algorithms and complexity, and interdisciplinary fields of studies, including bioinformatics, health-informatics, biostatistics, biomedical engineering, and viroinformatics. Khalid Raza is an Assistant Professor, the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. His research interests include translational bioinformatics, computational intelligence methods and its applications in bioinformatics, viroinformatics, and health informatics. Nilanjan Dey is an Associate Professor, the Department of Computer Science and Engineering, JIS University, Kolkata, India. His research interests include medical imaging, machine learning, computer-aided diagnosis, and data mining.

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models. - Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges - Presents integrative and multidisciplinary approaches to ubiquitous healthcare - Includes case studies to illustrate the concepts discussed

This issue of Surgical Pathology Clinics takes a departure from its presentation of Differential Diagnosis, Histopathology, Staging, and Prognosis of tumors in different anatomic sites. This special issue is devoted to topics in pathology informatics as they relate to the practice of surgical pathology. Topics include: Basics of Information Systems (Hardware, Software); Networks, Interfaces and Communications; Databases; Data Representation, Coding and Communication Standards; Laboratory Information Systems; Enhancing and Customizing Laboratory Information Systems to Improve/Enhance Pathologist Workflow; Laboratory Management and Operations; Specialized Laboratory Information Systems; Middleware and Laboratory Automation; Synoptic Reporting in Anatomical Pathology; Bar Coding and Tracking; Molecular Pathology Informatics; Informatics and Autopsy Pathology; Pathology Informatics and Project Management; Digital Imaging Basics; Use of Digital Images in Clinical Practice; Whole Slide Imaging; Telepathology; Mobile Technologies for the Surgical Pathologist; Image Analysis; Advanced Imaging Techniques; Healthcare Information Systems; Data Security and Reliability; Role of Informatics in Patient Safety and Quality Assurance; Role of Pathology Informatics in IT Leadership; Selection and Implementation of New Information Systems; Biomedical Informatics and Research Informatics; Training in Pathology Informatics; and Building Tools for the Surgical Pathologist: Next Generation Pathologist. Editor of this issue, Dr Anil Parwani, is Professor of Pathology and Biomedical Informatics and Director of Division of Pathology Informatics. Dr. Parwani is well known as expert in the area of Anatomical Pathology Informatics, which includes design of quality assurance tools, tissue banking informatics, clinical and research data integration and mining, synoptic reporting in anatomical pathology, clinical applications of whole slide imaging, digital imaging, telepathology, image analysis and lab automation and workflow processes, such as barcoding and voice recognition.

The book presents the results of studies on selected problems (such as predictive model of transcription initiation and termination, protein recognition codes, protein structure prediction, feature selection for disease prediction, information retrieval from medical imaging) of Bioinformatics and Information Retrieval. Information Retrieval is one of the contemporary answers to new challenges in threat evaluation of composite systems. This book provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. It describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles. It presents walk-throughs of data analysis tasks using different tools to help in taking decisions in healthcare management.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.

This book elucidates how genetic, biological and medical information can be applied to the development of personalized healthcare, medication and therapies. Focusing on aspects of the development of evidence-based approaches in bioinformatics and computational medicine, including data integration, methodologies, tools and models for clinical and translational medicine, it offers an essential introduction to clinical bioinformatics for clinical researchers and physicians, medical students and teachers, and scientists working with human disease-based omics and bioinformatics. Dr. Xiangdong Wang is a distinguished Professor of Medicine. He is Director of Shanghai Institute of Clinical Bioinformatics, Director of Fudan University Center for Clinical Bioinformatics, Deputy Director of Shanghai Respiratory Research Institute, Director of Biomedical Research Center, Fudan University Zhongshan Hospital, Shanghai, China; Dr. Christian Baumgartner is a Professor of Health Care and Biomedical Engineering at Institute of Health Care Engineering with European Notified Body of Medical Devices, Graz University of Technology, Graz, Austria; Dr. Denis Shields is a Professor of Clinical Bioinformatics at Conway Institute, Belfield, Dublin, Ireland; Dr. Hong-Wen Deng is a Professor at Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, USA; Dr. Jacques S Beckmann is a Professor and Director of Section of Clinical Bioinformatics, Swiss Institute of Bioinformatics, Switzerland.