Systems Analytics and Integration of Big Omics Data
Systems Analytics and Integration of Big Omics Data

Author: Gary Hardiman

Publisher: MDPI

Published: 2020-04-15

Total Pages: 202

ISBN-13: 3039287443

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A “genotype" is essentially an organism's full hereditary information which is obtained from its parents. A "phenotype" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

Systems Analytics and Integration of Big Omics Data
Systems Analytics and Integration of Big Omics Data

Author: Gary Hardiman

Publisher:

Published: 2020

Total Pages: 202

ISBN-13: 9783039287451

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A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene-environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome.

Big Data Analytics and Intelligence
Big Data Analytics and Intelligence

Author: Poonam Tanwar

Publisher: Emerald Group Publishing

Published: 2020-09-30

Total Pages: 252

ISBN-13: 1839091010

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Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

Big Data Analytics for Internet of Things
Big Data Analytics for Internet of Things

Author: Tausifa Jan Saleem

Publisher: John Wiley & Sons

Published: 2021-04-20

Total Pages: 402

ISBN-13: 1119740754

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BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.

Big Data Analytics in Bioinformatics and Healthcare
Big Data Analytics in Bioinformatics and Healthcare

Author: Wang, Baoying

Publisher: IGI Global

Published: 2014-10-31

Total Pages: 552

ISBN-13: 1466666129

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As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Smart Health Systems
Smart Health Systems

Author: Sonali Vyas

Publisher: Springer Nature

Published: 2021-08-24

Total Pages: 131

ISBN-13: 981164201X

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The upcoming trends in healthcare are intended towards improving the overall quality of life. In the past,management of health issues were limited to clinics and hospitals and managing patient’s data and analyzing it. This procedure was difficult and time consuming. A great effort was also needed in diagnosing the cause and type of disease, but this all has changed now. As advancement in research and technologies, a positive impact on healthcare is seen. This book assesses the need and era of smart healthcare and delivers content relevant to current age and time. It describes the trend, usage and practicality of IWMDs i.e. Wearable Medical Device or Sensors (WMSs) and Implantable Medical Devices (IMDs) and how they enhance the awareness of daily healthcare.It establishes a relation and conjunction of daily healthcare monitoring with clinical healthcare. A healthcare system is called smart when there is an ability to make decisions, which comes from data analytics. Smart healthcare systems possess capability of data analytics and IoT based services which can be implemented on smart phones using cloud technology. This book discusses various research trends and technologies related to innovations and advancements for smart healthcare systems. It also elaborates challenges, scope upcoming techniques, devices and future directions for smart healthcare systems.The proposed book would in particular benefit researchers interested in interdisciplinary sciences, It would also be of value to faculty, research communities, and researchers from diverse disciplines who aspire to create new and innovative research initiatives.

Applying Big Data Analytics in Bioinformatics and Medicine
Applying Big Data Analytics in Bioinformatics and Medicine

Author: Lytras, Miltiadis D.

Publisher: IGI Global

Published: 2017-06-16

Total Pages: 492

ISBN-13: 1522526080

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Many aspects of modern life have become personalized, yet healthcare practices have been lagging behind in this trend. It is now becoming more common to use big data analysis to improve current healthcare and medicinal systems, and offer better health services to all citizens. Applying Big Data Analytics in Bioinformatics and Medicine is a comprehensive reference source that overviews the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Featuring coverage on relevant topics that include smart data, proteomics, medical data storage, and drug design, this publication is an ideal resource for medical professionals, healthcare practitioners, academicians, and researchers interested in the latest trends and techniques in personalized medicine.

Applications of Big Data in Healthcare
Applications of Big Data in Healthcare

Author: Ashish Khanna

Publisher: Academic Press

Published: 2021-03-10

Total Pages: 311

ISBN-13: 0128204516

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Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians. The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data. - Provides case studies that illustrate the business processes underlying the use of big data and deep learning health analytics to improve health care delivery - Supplies readers with a foundation for further specialized study in clinical analysis and data management - Includes links to websites, videos, articles and other online content to expand and support the primary learning objectives for each major section of the book

Big Data in Oncology: Impact, Challenges, and Risk Assessment
Big Data in Oncology: Impact, Challenges, and Risk Assessment

Author: Neeraj Kumar Fuloria

Publisher: CRC Press

Published: 2023-12-21

Total Pages: 415

ISBN-13: 1000965260

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We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. This book focuses on targeted disease treatment using big data analytics. It provides information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data The most targeted or relevant audience is academics, research scholars, health care professionals, hospital management, pharmaceutical chemists, the biomedical industry, software engineers and IT professionals.