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Data Mining And Statistical Methods For Knowledge Discovery In Diseases Based On Multimodal Omics
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Book Synopsis Data Mining and Statistical Methods for Knowledge Discovery in Diseases Based on Multimodal Omics by : Jiajie Peng
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:
Book Synopsis High Dimensional Statistical and Computational Methods for Knowledge Discovery and Data Mining in Biomedical Data by : Funan Shi
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:
Book Synopsis Data Mining in Medical and Biological Research by : Eugenia Giannopoulou
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.
Book Synopsis Data Mining for Biomarker Discovery by : Panos M. Pardalos
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.
Book Synopsis Interactive Knowledge Discovery and Data Mining in Biomedical Informatics by : Andreas Holzinger
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 2014-07-16 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.
Book Synopsis Machine Learning Methods for Multi-Omics Data Integration by : Abedalrhman Alkhateeb
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.
Book Synopsis Intelligent data analysis in medicine and pharmacology by : Nada Lavrač
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.
Book Synopsis Methodologies of Multi-Omics Data Integration and Data Mining by : Kang Ning
Download or read book Methodologies of Multi-Omics Data Integration and Data Mining written by Kang Ning and published by Springer Nature. This book was released on 2023-01-15 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.
Book Synopsis Medical Data Mining and Knowledge Discovery by : Krzysztof J. Cios
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.
Book Synopsis Biological and Medical Data Analysis by : José María Barreiro
Download or read book Biological and Medical Data Analysis written by José María Barreiro and published by Springer Science & Business Media. This book was released on 2004-11-18 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Symposium on Biological and Medical Data Analysis, ISBMDA 2004, held in Barcelona, Spain in November 2004. The 50 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data analysis for image processing, data visualization, decision support systems, information retrieval, knowledge discovery and data mining, statistical methods and tools, time series analysis, data management and analysis in bioinformatics, integration of biological and medical data, metabolic data and pathways, and microarray data analysis and visualization.
Book Synopsis Big Data in Multimodal Medical Imaging by : Ayman El-Baz
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.
Author :José Luis Oliveira Publisher :Springer Science & Business Media ISBN 13 :9783540296744 Total Pages :424 pages Book Rating :4.2/5 (967 download)
Book Synopsis Biological and Medical Data Analysis by : José Luis Oliveira
Download or read book Biological and Medical Data Analysis written by José Luis Oliveira and published by Springer Science & Business Media. This book was released on 2005-11-04 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Symposium on Biological and Medical Data Analysis, ISBMDA 2005, held in Aveiro, Portugal, in November 2005. The 39 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on medical databases and information systems, data analysis and image processing, knowledge discovery and data mining, statistical methods and tools for biomedical data analysis, decision support systems, collaborative systems in biomedical informatics, as well as computational models, structural analysis, and microarray data analysis in the scope of bioinformatics.
Book Synopsis Big Data in Omics and Imaging by : MOMIAO. XIONG
Download or read book Big Data in Omics and Imaging written by MOMIAO. XIONG and published by CRC Press. This book was released on 2021-06-30 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Mining in Biomedical Imaging, Signaling, and Systems by : Sumeet Dua
Download or read book Data Mining in Biomedical Imaging, Signaling, and Systems written by Sumeet Dua and published by Auerbach Publications. This book was released on 2011-05-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedical and clinical applications of data mining. It supplies examples of frequently encountered heterogeneous data modalities and details the applicability of data mining approaches used to address the computational challenges in analyzing complex data. The book details feature extraction techniques and covers several critical feature descriptors. As machine learning is employed in many diagnostic applications, it covers the fundamentals, evaluation measures, and challenges of supervised and unsupervised learning methods. Both feature extraction and supervised learning are discussed as they apply to seizure-related patterns in epilepsy patients. Other specific disorders are also examined with regard to the value of data mining for refining clinical diagnoses, including depression and recurring migraines. The diagnosis and grading of the world’s fourth most serious health threat, depression, and analysis of acoustic properties that can distinguish depressed speech from normal are also described. Although a migraine is a complex neurological disorder, the text demonstrates how metabonomics can be effectively applied to clinical practice. The authors review alignment-based clustering approaches, techniques for automatic analysis of biofilm images, and applications of medical text mining, including text classification applied to medical reports. The identification and classification of two life-threatening heart abnormalities, arrhythmia and ischemia, are addressed, and a unique segmentation method for mining a 3-D imaging biomarker, exemplified by evaluation of osteoarthritis, is also presented. Given the widespread deployment of complex biomedical systems, the authors discuss system-engineering principles in a proposal for a design of reliable systems. This comprehensive volume demonstrates the broad scope of uses for data mining and includes detailed strategies and methodologies for analyzing data from biomedical images, signals, and systems.
Book Synopsis Data Mining and Machine Learning for Biomedical Applications by : Erin Teeple
Download or read book Data Mining and Machine Learning for Biomedical Applications written by Erin Teeple and published by Academic Press. This book was released on 2022-03-15 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Machine Learning for Biomedical Applications is a rigorous practical introduction to the fundamentals of data science. It discusses topics such as data integration and management; statistical methods of data science; methodological approaches used for data mining and knowledge discovery with biomedical domain examples; the core principles and methods of hypothesis-driven statistical analyses; differences and relative benefits of machine learning approaches; predictive model performance assessment; and concepts of bias and variance with respect to the design and evaluation of predictive models. A final chapter presents considerations and limitations when applying and interpreting data science models in biological science and bioengineering. For graduate students, this book offers a comprehensive methods introduction, making it ideal to accompany a course in this area. It is also useful for established engineers and scientists who wish to explore data mining or predictive analytics within their domains of expertise. This reference is fully supported with exercises, discussion questions, code vignettes, and code files with demonstration code. This presentation of coded solutions has been prepared with readers in mind who have limited coding experience. The fully coded methods are presented in both R and Python. The foundational principles covered in this book can be applied by readers when creating new tools for diagnosis, monitoring, information visualization, and robotic intervention. A unique and foundational resource that offers a mastery of foundational concepts and skills for data management and analysis Presents statistical concepts with examples and exercises for a biomedical engineering audience Introduces the underlying principles, conceptual differences, and limitations of statistical learning approaches
Book Synopsis Statistical Methods and Applications for Biomarker Discovery Using Large Scale Omics Data Set by : Sahar Ghasemi
Download or read book Statistical Methods and Applications for Biomarker Discovery Using Large Scale Omics Data Set written by Sahar Ghasemi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Urinary albumin to creatinine ratio (UACR), Estimated glomerular filtration rate (eGFR), Genome-wide association studies (GWAS), Expression quantitative trait loci (eQTL), Conditional association analysis, SNP-specific alpha-level, Colocalization.
Book Synopsis Integrating Omics Data by : George C. Tseng
Download or read book Integrating Omics Data written by George C. Tseng and published by . This book was released on 2015 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.
