Methodologies Of Multi Omics Data Integration And Data Mining
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Methodologies Of Multi Omics Data Integration And Data Mining
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Author : Kang Ning
language : en
Publisher:
Release Date : 2023
Methodologies Of Multi Omics Data Integration And Data Mining written by Kang Ning and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
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.
Machine Learning Methods For Multi Omics Data Integration
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Author : Abedalrhman Alkhateeb
language : en
Publisher: Springer
Release Date : 2023-11-14
Machine Learning Methods For Multi Omics Data Integration written by Abedalrhman Alkhateeb and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-14 with Science categories.
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.
Traditional Chinese Medicine And Diseases
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Author : Kang Ning
language : en
Publisher: Springer Nature
Release Date : 2022-10-03
Traditional Chinese Medicine And Diseases written by Kang Ning and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-03 with Medical categories.
This book focuses on the multi-omics big-data integration, the data-mining techniques and the cutting-edge omics researches in principles and applications for a deep understanding of Traditional Chinese Medicine (TCM) and diseases from the following aspects: (1) Basics about multi-omics data and analytical methods for TCM and diseases. (2) The needs of omics studies in TCM researches, and the basic background of omics research in TCM and disease. (3) Better understanding of the multi-omics big-data integration techniques. (4) Better understanding of the multi-omics big-data mining techniques, as well as with different applications, for most insights from these omics data for TCM and disease researches. (5) TCM preparation quality control for checking both prescribed and unexpected ingredients including biological and chemical ingredients. (6) TCM preparation source tracking. (7) TCM preparation network pharmacology analysis. (8) TCM analysis data resources, web services, and visualizations. (9) TCM geoherbalism examination and authentic TCM identification. Traditional Chinese Medicine has been in existence for several thousands of years, and only in recent tens of years have we realized that the researches on TCM could be profoundly boosted by the omics technologies. Devised as a book on TCM and disease researches in the omics age, this book has put the focus on data integration and data mining methods for multi-omics researches, which will be explained in detail and with supportive examples the “What”, “Why” and “How” of omics on TCM related researches. It is an attempt to bridge the gap between TCM related multi-omics big data, and the data-mining techniques, for best practice of contemporary bioinformatics and in-depth insights on the TCM related questions.
Synthetic Biology And Igem Techniques Development And Safety Concerns
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Author : Kang Ning
language : en
Publisher: Springer Nature
Release Date : 2023-06-19
Synthetic Biology And Igem Techniques Development And Safety Concerns written by Kang Ning and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Science categories.
This book focuses on biological engineering techniques, multi-omics big-data integration, and data-mining techniques, as well as cutting-edge researches in principles and applications of several synthetic biology applications. Synthetic biology is a new research area, while it has been rooted from the long-established area including biological engineering, metabolite engineering, and systems biology. This book will discuss the following aspects: (1) introduction to synthetic biology and iGEM, especially focusing on the systematic design, rational engineering, and sustainability of design in the omics ages; (2) synthetic biology–related multi-omics data integration and data mining techniques; (3) the technical issues, development issues, and safety issues of synthetic biology; (4) data resources, web services, and visualizations for synthetic biology; and (5) advancement in concrete research on synthetic biology, with several case studies shown. Devised as a book on synthetic biology research and education in the omics age, this book has put focuses on systematic design, rational engineering, and sustainability of design for synthetic biology, which will explain in detail and with supportive examples the “What,” “Why,” and “How” of the topic. It is an attempt to bridge the gap between synthetic biology’s research and education side, for best practice of synthetic biology and in-depth insights for the related questions.
Methodologies Of Multi Omics Data Integration And Data Mining
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Author : Kang Ning
language : en
Publisher: Springer Nature
Release Date : 2023-01-15
Methodologies Of Multi Omics Data Integration And Data Mining written by Kang Ning and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-15 with Medical categories.
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.
Multi Omic Data Integration In Oncology
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Author : Chiara Romualdi
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-03
Multi Omic Data Integration In Oncology written by Chiara Romualdi and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Medical categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Multi Omic Data Integration
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Author : Paolo Tieri
language : en
Publisher: Frontiers Media SA
Release Date : 2015-09-17
Multi Omic Data Integration written by Paolo Tieri and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-17 with Electronic book categories.
Stable, predictive biomarkers and interpretable disease signatures are seen as a significant step towards personalized medicine. In this perspective, integration of multi-omic data coming from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strategy to reconstruct and analyse complex multi-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data –although often publicly and freely available- lie in databases and repositories underutilised or not used at all. Issues coming from lack of standardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as intertwined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. Papers discuss data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of specific diseases or in identifying candidate biomarkers to exploit the full benefit of multi-omic datasets and their intrinsic information content. Topics of interest include, but are not limited to: • Methods for the integration of layered data, including, but not limited to, genomics, transcriptomics, glycomics, proteomics, metabolomics; • Application of multi-omic data integration approaches for diagnostic biomarker discovery in any field of the life sciences; • Innovative approaches for the analysis and the visualization of multi-omic datasets; • Methods and applications for systematic measurements from single/undivided samples (comprising genomic, transcriptomic, proteomic, metabolomic measurements, among others); • Multi-scale approaches for integrated dynamic modelling and simulation; • Implementation of applications, computational resources and repositories devoted to data integration including, but not limited to, data warehousing, database federation, semantic integration, service-oriented and/or wiki integration; • Issues related to the definition and implementation of standards, shared identities and semantics, with particular focus on the integration problem. Research papers, reviews and short communications on all topics related to the above issues were welcomed.
Systems Analytics And Integration Of Big Omics Data
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Author : Gary Hardiman
language : en
Publisher: MDPI
Release Date : 2020-04-15
Systems Analytics And Integration Of Big Omics Data written by Gary Hardiman and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-15 with Science categories.
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.
Multivariate Data Integration Using R
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Author : Kim-Anh Lê Cao
language : en
Publisher: CRC Press
Release Date : 2021-11-08
Multivariate Data Integration Using R written by Kim-Anh Lê Cao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Computers categories.
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
Advances In Methods And Tools For Multi Omics Data Analysis
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Author : Ornella Cominetti
language : en
Publisher: Frontiers Media SA
Release Date : 2023-05-12
Advances In Methods And Tools For Multi Omics Data Analysis written by Ornella Cominetti and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Science categories.