Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging
Author: Ayman El-Baz
Publisher: CRC Press
Total Pages: 264
Release: 2019-11-05
Genre: Computers
ISBN: 1351380729
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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.

Data Mining in Bioinformatics

Data Mining in Bioinformatics
Author: Jason T. L. Wang
Publisher: Springer Science & Business Media
Total Pages: 356
Release: 2005
Genre: Computers
ISBN: 9781852336714
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Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Learning to Classify Text Using Support Vector Machines

Learning to Classify Text Using Support Vector Machines
Author: Thorsten Joachims
Publisher: Springer Science & Business Media
Total Pages: 228
Release: 2002-04-30
Genre: Computers
ISBN: 079237679X
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Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

Innovating Health Against Future Pandemics

Innovating Health Against Future Pandemics
Author: Simona Mellino
Publisher: Elsevier
Total Pages: 202
Release: 2024-04-30
Genre: Science
ISBN: 0443136823
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Innovating Health Against Future Pandemics covers the key aspects which drive heterogeneity in an individual's response to COVID-19, including age, sex, genetic makeup, immune responses, comorbidities, and viral strains/loads. This book also reviews the case examples from other disciplines to highlight areas where precision medicine and AI could be applied for the improvement of pandemic management. This includes research, primary and secondary prevention, isolation/tracking, hospitalization and patient management, diagnosis, and treatments. Lastly, drawing on past experiences for each of the areas this book provides practical recommendations to manage future pandemics. COVID-19 offered an unprecedented occasion to test the impact of digitally enabled solutions within precision medicine for public health and for accelerating their deployment and adoption. - Explores the benefits of AI technologies in triage, diagnosis, and risk prediction - Reviews the innovative clinical trial designs in terms of platforms and decentralization - Covers Healthcare workload, including remote monitoring to help prevent burnout

World Health Statistics 2016 [OP]

World Health Statistics 2016 [OP]
Author: World Health Organization
Publisher: World Health Organization
Total Pages: 131
Release: 2016-06-08
Genre: Medical
ISBN: 9241565268
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The World Health Statistics series is WHO's annual compilation of health statistics for its 194 Member States. The World Health Statistics 2016focuses on the health and health-related Sustainable Development Goals (SDGs) and associated targets. It represents an initial effort to bring together available data on SDG health and health-related indicators, providing an assessment of the situation in 2016. The SDG health goal -- ensure healthy lives and promote well-being for all at all ages -- includes 13 targets, covering all major health priorities, and including the unfinished and expanded Millennium Development Goals (MDGs) agenda, four targets to address noncommunicable diseases (NCDs), mental health, injuries and environmental issues, and four "means of implementation" targets. This report also seeks to demonstrate the critical linkages between health and other SDGs by including indicators of selected health determinants and risk factors in other SDG targets. The series is produced by the WHO Department of Information, Evidence and Research, of the Health Systems and Innovation Cluster. As in previous years, World Health Statistics 2016 has been compiled using publications and databases produced and maintained by WHO technical programs and regional offices. WHO presents World Health Statistics 2016as an integral part of its ongoing efforts to provide enhanced access to comparable high-quality statistics on core measures of population health and national health systems. Unless otherwise stated, all estimates have been cleared following consultation with Member States and are published here as official WHO figures.

Data Analytics in Bioinformatics

Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy
Publisher: John Wiley & Sons
Total Pages: 433
Release: 2021-01-20
Genre: Computers
ISBN: 111978560X
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Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

Knowledge Graphs and Big Data Processing

Knowledge Graphs and Big Data Processing
Author: Valentina Janev
Publisher: Springer Nature
Total Pages: 212
Release: 2020-07-15
Genre: Computers
ISBN: 3030531996
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This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

DNA Methylation

DNA Methylation
Author: J. Jost
Publisher: Birkhäuser
Total Pages: 581
Release: 2013-11-11
Genre: Science
ISBN: 3034891180
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The occurrence of 5-methylcytosine in DNA was first described in 1948 by Hotchkiss (see first chapter). Recognition of its possible physiologi cal role in eucaryotes was first suggested in 1964 by Srinivasan and Borek (see first chapter). Since then work in a great many laboratories has established both the ubiquity of 5-methylcytosine and the catholicity of its possible regulatory function. The explosive increase in the number of publications dealing with DNA methylation attests to its importance and makes it impossible to write a comprehensive coverage of the literature within the scope of a general review. Since the publication of the 3 most recent books dealing with the subject (DNA methylation by Razin A. , Cedar H. and Riggs A. D. , 1984 Springer Verlag; Molecular Biology of DNA methylation by Adams R. L. P. and Burdon R. H. , 1985 Springer Verlag; Nucleic Acids Methylation, UCLA Symposium suppl. 128, 1989) considerable progress both in the techniques and results has been made in the field of DNA methylation. Thus we asked several authors to write chapters dealing with aspects of DNA methyla tion in which they are experts. This book should be most useful for students, teachers as well as researchers in the field of differentiation and gene regulation. We are most grateful to all our colleagues who were willing to spend much time and effort on the publication of this book. We also want to express our gratitude to Yan Chim Jost for her help in preparing this book.