Data Mining And Statistical Methods For Knowledge Discovery In Diseases Based On Multimodal Omics

Author : Jiajie Peng
Publisher : Frontiers Media SA
ISBN 13 : 2889761746
Total Pages : 160 pages
Book Rating : 4.46/5 ( download)

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Download or read book Data Mining and Statistical Methods for Knowledge Discovery in Diseases Based on Multimodal Omics written by Jiajie Peng and published by Frontiers Media SA. This book was released on 2022-06-06 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Funan Shi
Publisher :
ISBN 13 :
Total Pages : 106 pages
Book Rating : 4.24/5 ( download)

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Download or read book High Dimensional Statistical and Computational Methods for Knowledge Discovery and Data Mining in Biomedical Data written by Funan Shi and published by . This book was released on 2018 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Eugenia Giannopoulou
Publisher : BoD – Books on Demand
ISBN 13 : 9537619303
Total Pages : 334 pages
Book Rating : 4.05/5 ( download)

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Download or read book Data Mining in Medical and Biological Research written by Eugenia Giannopoulou and published by BoD – Books on Demand. This book was released on 2008-11-01 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to bring together the most recent advances and applications of data mining research in the promising areas of medicine and biology from around the world. It consists of seventeen chapters, twelve related to medical research and five focused on the biological domain, which describe interesting applications, motivating progress and worthwhile results. We hope that the readers will benefit from this book and consider it as an excellent way to keep pace with the vast and diverse advances of new research efforts.

Author : Andreas Holzinger
Publisher : Springer
ISBN 13 : 9783662439678
Total Pages : 357 pages
Book Rating : 4.70/5 ( download)

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Download or read book Interactive Knowledge Discovery and Data Mining in Biomedical Informatics written by Andreas Holzinger and published by Springer. This book was released on 2014-06-26 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Author : Panos M. Pardalos
Publisher : Springer Science & Business Media
ISBN 13 : 1461421071
Total Pages : 256 pages
Book Rating : 4.78/5 ( download)

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Download or read book Data Mining for Biomarker Discovery written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2012-02-11 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics. This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques. This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as well engineers and applied scientists interested in the interdisciplinary application of data mining techniques.

Author : Abedalrhman Alkhateeb
Publisher : Springer
ISBN 13 : 9783031365010
Total Pages : 0 pages
Book Rating : 4.11/5 ( download)

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Download or read book Machine Learning Methods for Multi-Omics Data Integration written by Abedalrhman Alkhateeb and published by Springer. This book was released on 2023-11-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advancement of biomedical engineering has enabled the generation of multi-omics data by developing high-throughput technologies, such as next-generation sequencing, mass spectrometry, and microarrays. Large-scale data sets for multiple omics platforms, including genomics, transcriptomics, proteomics, and metabolomics, have become more accessible and cost-effective over time. Integrating multi-omics data has become increasingly important in many research fields, such as bioinformatics, genomics, and systems biology. This integration allows researchers to understand complex interactions between biological molecules and pathways. It enables us to comprehensively understand complex biological systems, leading to new insights into disease mechanisms, drug discovery, and personalized medicine. Still, integrating various heterogeneous data types into a single learning model also comes with challenges. In this regard, learning algorithms have been vital in analyzing and integrating these large-scale heterogeneous data sets into one learning model. This book overviews the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validation. It covers different types of learning for supervised and unsupervised learning techniques, including standard classifiers, deep learning, tensor factorization, ensemble learning, and clustering, among others. The book categorizes different levels of integrations, ranging from early, middle, or late-stage among multi-view models. The underlying models target different objectives, such as knowledge discovery, pattern recognition, disease-related biomarkers, and validation tools for multi-omics data. Finally, the book emphasizes practical applications and case studies, making it an essential resource for researchers and practitioners looking to apply machine learning to their multi-omics data sets. The book covers data preprocessing, feature selection, and model evaluation, providing readers with a practical guide to implementing machine learning techniques on various multi-omics data sets.

Author : Nada Lavrač
Publisher :
ISBN 13 : 9781461560609
Total Pages : 336 pages
Book Rating : 4.08/5 ( download)

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Download or read book Intelligent data analysis in medicine and pharmacology written by Nada Lavrač and published by . This book was released on 1997 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent data analysis, data mining and knowledge discovery in databases have recently gained the attention of a large number of researchers and practitioners. This is witnessed by the rapidly increasing number of submissions and participants at related conferences and workshops, by the emergence of new journals in this area (e.g., Data Mining and Knowledge Discovery, Intelligent Data Analysis, etc.), and by the increasing number of new applications in this field. In our view, the awareness of these challenging research fields and emerging technologies has been much larger in industry than in medicine and pharmacology. The main purpose of this book is to present the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96), IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of the book is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.

Author : Krzysztof J. Cios
Publisher : Physica
ISBN 13 :
Total Pages : 528 pages
Book Rating : 4.17/5 ( download)

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Download or read book Medical Data Mining and Knowledge Discovery written by Krzysztof J. Cios and published by Physica. This book was released on 2001-01-12 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine. This book addresses all these specific features.

Author : 吴晔
Publisher :
ISBN 13 :
Total Pages : 75 pages
Book Rating : 4.71/5 ( download)

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Download or read book Accurate Text and Data Mining Methods for Biomedical Knowledge Discovery written by 吴晔 and published by . This book was released on 2020 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Ayman El-Baz
Publisher : CRC Press
ISBN 13 : 1351380729
Total Pages : 247 pages
Book Rating : 4.20/5 ( download)

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Download or read book Big Data in Multimodal Medical Imaging written by Ayman El-Baz and published by CRC Press. This book was released on 2019-11-05 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.