Big Data In Omics And Imaging

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Big Data In Radiation Oncology

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Author : Jun Deng
language : en
Publisher: CRC Press
Release Date : 2019-03-07

Big Data In Radiation Oncology written by Jun Deng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-07 with Science categories.

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Big Data In Omics And Imaging

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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2017-12-01

Big Data In Omics And Imaging written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-01 with Mathematics categories.

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

Big Data In Omics And Imaging

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Author : Momiao Xiong
language : en
Publisher: CRC Press
Release Date : 2018-06-14

Big Data In Omics And Imaging written by Momiao Xiong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-14 with Mathematics categories.

Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic and imaging data analysis and causal inference in big data era. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), genome-wide expression studies (GWES), and epigenome-wide association studies (EWAS), the overall contribution of the new identified genetic variants is small and a large fraction of genetic variants is still hidden. Understanding the etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal revolution to developing a new generation of genetic analysis for shifting the current paradigm of genetic analysis from shallow association analysis to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism of complex diseases. FEATURES Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies. Bridge the gap between the traditional association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease Develop causal machine learning methods integrating causal inference and machine learning Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks The book is designed for graduate students and researchers in genomics, epigenomics, medical image, bioinformatics, and data science. Topics covered are: mathematical formulation of causal inference, information geometry for causal inference, topology group and Haar measure, additive noise models, distance correlation, multivariate causal inference and causal networks, dynamic causal networks, multivariate and functional structural equation models, mixed structural equation models, causal inference with confounders, integer programming, deep learning and differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution, cell –specific causal networks, deep learning for image segmentation and image analysis, imaging and genomic data analysis, integrated multilevel causal genomic, epigenomic and imaging data analysis.

Biocomputing 2022 Proceedings Of The Pacific Symposium

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Author : Russ B Altman
language : en
Publisher: World Scientific
Release Date : 2021-11-29

Biocomputing 2022 Proceedings Of The Pacific Symposium written by Russ B Altman and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Science categories.

The Pacific Symposium on Biocomputing (PSB) 2022 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2022 will be held on January 3 - 7, 2022 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2022 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Cognitive Intelligence And Big Data In Healthcare

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Author : D. Sumathi
language : en
Publisher: John Wiley & Sons
Release Date : 2022-08-23

Cognitive Intelligence And Big Data In Healthcare written by D. Sumathi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-23 with Computers categories.

COGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area. Audience The book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine.

Radiomics And Radiogenomics

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Author : Ruijiang Li
language : en
Publisher: CRC Press
Release Date : 2019-07-09

Radiomics And Radiogenomics written by Ruijiang Li and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-09 with Science categories.

Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation

Big Data Analytics For Healthcare

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Author : Pantea Keikhosrokiani
language : en
Publisher: Academic Press
Release Date : 2022-05-19

Big Data Analytics For Healthcare written by Pantea Keikhosrokiani and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-19 with Medical categories.

Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. - Presents theories, methods and approaches in which data analytic techniques are used for medical data - Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases - Discusses social, behavioral, and medical fake news analytics for medical information systems

Intelligent Manufacturing And Energy Sustainability

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Author : A.N.R. Reddy
language : en
Publisher: Springer Nature
Release Date : 2021-04-02

Intelligent Manufacturing And Energy Sustainability written by A.N.R. Reddy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-02 with Technology & Engineering categories.

This book includes best selected, high-quality research papers presented at the International Conference on Intelligent Manufacturing and Energy Sustainability (ICIMES 2020) held at the Department of Mechanical Engineering, Malla Reddy College of Engineering & Technology (MRCET), Maisammaguda, Hyderabad, India, during August 21-22, 2020. It covers topics in the areas of automation, manufacturing technology and energy sustainability and also includes original works in the intelligent systems, manufacturing, mechanical, electrical, aeronautical, materials, automobile, bioenergy and energy sustainability.

Biocomputing 2025 Proceedings Of The Pacific Symposium

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Author : Russ B Altman
language : en
Publisher: World Scientific
Release Date : 2024-11-29

Biocomputing 2025 Proceedings Of The Pacific Symposium written by Russ B Altman and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Science categories.

The Pacific Symposium on Biocomputing (PSB) 2025 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2025 will be held on January 4 - 8, 2025 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2025 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.

Biological Networks In Human Health And Disease

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Author : Romana Ishrat
language : en
Publisher: Springer Nature
Release Date : 2023-10-13

Biological Networks In Human Health And Disease written by Romana Ishrat and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Science categories.

This book presents methods and tools of network biology and bioinformatics for understanding the disease dynamics and identification of drug targets. The initial section of chapters introduce the theoretical aspects followed by the different applications for construction and analysis of biological networks, methods for identifying crucial nodes in networks, and network dynamics. The book covers the latest advances in the network medicine, exploring the different types of biological networks, and their applications. It further reviews the role of R language in the network-based approaches that help in understanding biological systems and identifying biological functions. Towards the end, the book explores the recent developments and applications in machine learning and its potential for advancing network biology. Finally, the book elucidates a comprehensive yet a representative description of challenges associated with the understanding of disease dynamics using network biology. Given its scope, the book is intended for researchers and advanced postgraduate students of bioinformatics, computational biology, and medical sciences. ​