Big Data in Omics and Imaging

The number of parameters being estimated will be very large. For example, if we assume that an imaging has 100 × 100 pixels, the number of neurons that are connected with the neurons in the input should be 10,000.

Author: Momiao Xiong

Publisher: CRC Press

ISBN: 9781351172639

Category: Mathematics

Page: 736

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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.
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Big Data in Omics and Imaging

The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data.

Author: Momiao Xiong

Publisher: CRC Press

ISBN: 9781498725804

Category: Mathematics

Page: 668

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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.
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Big Data in Omics and Imaging

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 ...

Author: Momiao Xiong

Publisher: CRC Press

ISBN: 9781351172622

Category: Mathematics

Page: 736

View: 272

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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.
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Big Data in Radiation Oncology

In addition, with the high dimensionality of data, computation is often much more challenging. For applications of the Bayesian technique to omics and imaging oncology studies, see Lee et al. (2003), Sha et al.

Author: Jun Deng

Publisher: CRC Press

ISBN: 9781351801119

Category: Science

Page: 289

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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.
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Big Data in Omics and Imaging Two Volume Set

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 ...

Author: Taylor & Francis Group

Publisher:

ISBN: 0367002183

Category:

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Introduction to Proteins

... Second Edition Darren J. Wilkinson Bioinformatics: A Practical Approach Shui Qing Ye Introduction to Computational Proteomics Golan Yona Big Data in Omics and Imaging: Association Analysis Momiao Xiong Big Data in Omics and Imaging: ...

Author: Amit Kessel

Publisher: CRC Press

ISBN: 9781498747189

Category: Computers

Page: 932

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Introduction to Proteins provides a comprehensive and state-of-the-art introduction to the structure, function, and motion of proteins for students, faculty, and researchers at all levels. The book covers proteins and enzymes across a wide range of contexts and applications, including medical disorders, drugs, toxins, chemical warfare, and animal behavior. Each chapter includes a Summary, Exercies, and References. New features in the thoroughly-updated second edition include: A brand-new chapter on enzymatic catalysis, describing enzyme biochemistry, classification, kinetics, thermodynamics, mechanisms, and applications in medicine and other industries. These are accompanied by multiple animations of biochemical reactions and mechanisms, accessible via embedded QR codes (which can be viewed by smartphones) An in-depth discussion of G-protein-coupled receptors (GPCRs) A wider-scale description of biochemical and biophysical methods for studying proteins, including fully accessible internet-based resources, such as databases and algorithms Animations of protein dynamics and conformational changes, accessible via embedded QR codes Additional features Extensive discussion of the energetics of protein folding, stability and interactions A comprehensive view of membrane proteins, with emphasis on structure-function relationship Coverage of intrinsically unstructured proteins, providing a complete, realistic view of the proteome and its underlying functions Exploration of industrial applications of protein engineering and rational drug design Each chapter includes a Summary, Exercies, and References Approximately 300 color images Downloadable solutions manual available at www.crcpress.com For more information, including all presentations, tables, animations, and exercises, as well as a complete teaching course on proteins' structure and function, please visit the author's website: http://ibis.tau.ac.il/wiki/nir_bental/index.php/Introduction_to_Proteins_Book. Praise for the first edition "This book captures, in a very accessible way, a growing body of literature on the structure, function and motion of proteins. This is a superb publication that would be very useful to undergraduates, graduate students, postdoctoral researchers, and instructors involved in structural biology or biophysics courses or in research on protein structure-function relationships." --David Sheehan, ChemBioChem, 2011 "Introduction to Proteins is an excellent, state-of-the-art choice for students, faculty, or researchers needing a monograph on protein structure. This is an immensely informative, thoroughly researched, up-to-date text, with broad coverage and remarkable depth. Introduction to Proteins would provide an excellent basis for an upper-level or graduate course on protein structure, and a valuable addition to the libraries of professionals interested in this centrally important field." --Eric Martz, Biochemistry and Molecular Biology Education, 2012
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Intelligent Manufacturing and Energy Sustainability

Several famous big data biomedical projects which generated large-scale sequencing data include 1,000 Genome Project, ... data sources on which big data analytics is applied are Electronic Health Records (EHRs), Omics, medical imaging, ...

Author: A.N.R. Reddy

Publisher: Springer Nature

ISBN: 9789813344433

Category: Technology & Engineering

Page: 794

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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.
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Biomedical Data Mining for Information Retrieval

The types of healthcare data sources on which big data analytics is applied are EHRs (Electronic Health Records), Omics, medical imaging, clinical texts, etc. 7.2.1 Big EHR Data The principal source of Big Data in human health are ...

Author: Subhendu Kumar Pani

Publisher: John Wiley & Sons

ISBN: 9781119711261

Category: Computers

Page: 448

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This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.
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Big Data in Multimodal Medical Imaging

K.-A. Lê Cao, D. Rossouw, C. Robert-Granié, and P. Besse, “A sparse PLS for variable selection when integrating omics data,” Statistical Applications in Genetics and Molecular Biology, vol. 7, no.

Author: Ayman El-Baz

Publisher: CRC Press

ISBN: 9781351380737

Category: Computers

Page: 330

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There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.
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Biocomputing 2022 Proceedings Of The Pacific Symposium

Alzheimer's Disease Neuroimaging Initiative (ADNI), Psychiatric Genomics Consortium (PGC), The Cancer Imaging ... The submission by Mo et al. proposes to take advantage of the multi-omics nature of the Big Data imaging datasets to ...

Author: Russ B Altman

Publisher: World Scientific

ISBN: 9789811250484

Category: Science

Page: 380

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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.
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