Biological Data Integration
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Biological Data Integration
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Author : Christine Froidevaux
language : en
Publisher: John Wiley & Sons
Release Date : 2024-01-04
Biological Data Integration written by Christine Froidevaux and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-04 with Computers categories.
The study of biological data is constantly undergoing profound changes. Firstly, the volume of data available has increased considerably due to new high throughput techniques used for experiments. Secondly, the remarkable progress in both computational and statistical analysis methods and infrastructures has made it possible to process these voluminous data. The resulting challenge concerns our ability to integrate these data, i.e. to use their complementary nature effectively in the hope of advancing our knowledge. Therefore, a major challenge in studying biology today is integrating data for the most exhaustive analysis possible. Biological Data Integration deals in a pedagogical way with research work in biological data science, examining both computational approaches to data integration and statistical approaches to the integration of omics data.
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 Mathematics 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.
Data Integration In The Life Sciences
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Author : Patrick Lambrix
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-08-11
Data Integration In The Life Sciences written by Patrick Lambrix and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-11 with Science categories.
The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglydepends on the development of models of biological systems. The development of these models often requires integration of diverse experimental data resources; once constructed, the models themselves become data and present new integration challenges for tasks such as interpretation, validation and comparison. The Data Integration in the Life Sciences (DILS) Conference series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences. DILS 2010 was the seventh event in the series and was held in Goth- burg, Sweden during August 25–27, 2010. The associated proceedings contain 14 peer-reviewed papers and 2 invited papers. The sessions addressed ontology engineering, and in particular, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for data integration in the life sciences; data and text mining techniques for discovering and recognizing biomedical entities and relationships between these entities; and information management, introducing data integration solutions for di?erent types of applications related to cancer, systems biology and - croarray experimental data, and an approach for integrating ranked data in the life sciences.
Data Integration In The Life Sciences
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Author : Erhard Rahm
language : en
Publisher: Springer
Release Date : 2004-02-27
Data Integration In The Life Sciences written by Erhard Rahm and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-02-27 with Computers categories.
This book constitutes the refereed proceedings of the First International Workshop on Data Integration in the Life Sciences, DILS 2004, held in Leipzig, Germany, in March 2004. The 13 revised full papers and 2 revised short papers presented were carefully reviewed and selected from many submissions. The papers are organized in topical sections on scientific and clinical workflows, ontologies and taxonomies, indexing and clustering, integration tools and systems, and integration techniques.
Data Integration In The Life Sciences
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Author : Amos Bairoch
language : en
Publisher: Springer
Release Date : 2008-06-17
Data Integration In The Life Sciences written by Amos Bairoch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-17 with Medical categories.
For several years now, there has been an exponential growth of the amount of life science data (e. g. , sequenced complete genomes, 3D structures, DNA chips, mass spectroscopy data), most of which are generated by high-throughput - periments. This exponentialcorpusof data is storedand made availablethrough a large number of databases and resources over the Web, but unfortunately still with a high degreeof semantic heterogeneity and varying levels of quality. These data must be combined together and processed by bioinformatics tools deployed on powerful and e?cient platforms to permit the uncovering of patterns, s- ilarities and in general to help in the process of discovery. Analyzing complex, voluminous, and heterogeneous data and guiding the analysis of data are thus of paramount importance and necessitate the involvement of data integration techniques. DILS 2008 was the ?fth in a workshop series that aims at fostering disc- sion, exchange, and innovation in research and development in the area of data integration for the life sciences. Each previous DILS workshop attracted around 100 researchers from all over the world and saw an increase of submitted - pers over the preceding one. This year was not an exception and the number of submitted papers increased to 54. The ProgramCommittee selected 18 of them. The selected papers cover a wide spectrum of theoretical and practical issues including data annotation, Semantic Web for the life sciences, and data mining on integrated biological data.
Data Integration In The Life Sciences
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Author : Bertram Ludäscher
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-07-08
Data Integration In The Life Sciences written by Bertram Ludäscher and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-08 with Computers categories.
This book constitutes the refereed proceedings of the Second International Workshop on Data Integration in the Life Sciences, DILS 2005, held in San Diego, CA, USA in July 2005. The 20 revised full papers presented together with 8 revised posters and demonstration papers, 2 keynote articles and 5 invited position statements were carefully reviewed and selected from 50 initial submissions. The papers are organized in topical sections on user applications, ontologies, data integration, and others and address all current issues in data integration from the life science point of view.
Integrative Bioinformatics
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Author : Ming Chen
language : en
Publisher: Springer Nature
Release Date : 2022-04-15
Integrative Bioinformatics written by Ming Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-15 with Medical categories.
This book provides an overview of the history of integrative bioinformatics and the actual situation and the relevant tools. Subjects cover the essential topics, basic introductions, and latest developments; biological data integration and manipulation; modeling and simulation of networks; as well as a number of applications of integrative bioinformatics. It aims to provide basic introduction of biological information systems and guidance for the computational analysis of systems biology. This book covers a range of issues and methods that unveil a multitude of omics data integration and relevance that integrative bioinformatics has today. It contains a unique compilation of invited and selected articles from the Journal of Integrative Bioinformatics (JIB) and annual meetings of the International Symposium on Integrative Bioinformatics.
Data Integration In The Life Sciences
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Author : Patrick Lambrix
language : en
Publisher: Springer
Release Date : 2010-08-19
Data Integration In The Life Sciences written by Patrick Lambrix and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-19 with Science categories.
The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglydepends on the development of models of biological systems. The development of these models often requires integration of diverse experimental data resources; once constructed, the models themselves become data and present new integration challenges for tasks such as interpretation, validation and comparison. The Data Integration in the Life Sciences (DILS) Conference series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences. DILS 2010 was the seventh event in the series and was held in Goth- burg, Sweden during August 25–27, 2010. The associated proceedings contain 14 peer-reviewed papers and 2 invited papers. The sessions addressed ontology engineering, and in particular, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for data integration in the life sciences; data and text mining techniques for discovering and recognizing biomedical entities and relationships between these entities; and information management, introducing data integration solutions for di?erent types of applications related to cancer, systems biology and - croarray experimental data, and an approach for integrating ranked data in the life sciences.
Machine Learning Methods For Multi Omics Data Integration
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Author : Abedalrhman Alkhateeb
language : en
Publisher: Springer Nature
Release Date : 2023-11-13
Machine Learning Methods For Multi Omics Data Integration written by Abedalrhman Alkhateeb and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-13 with Computers 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 integratingthese 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.
Biological Data Integration
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Author : Daniel Sobral
language : en
Publisher:
Release Date : 2009
Biological Data Integration written by Daniel Sobral and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
The growing complexity of biological questions and the exponential increase in available data to explore these questions are making the management and integration of such information a serious issue for a successful research activity. I therefore aimed at exploring how data integration techniques could be used to answer complex biological problems. In this context, the ANISEED database emerged as an ideal data integration platform, given its rich representation of concepts related to organism development, the availability of large volumes of multiple types of experimental data and the relative simplicity of the ascidian model system. I thus used this rich resource to explore complex biological questions: 1) is the chordate body plan found both in ascidians and vertebrates the result of a common genetic program? By systematically comparing all available gene expression patterns from ANISEED and ZFIN (for zebrafish) I observed a generalized divergence in the gene expression programs of both species, with some similarity noticeable in the locomotory system; 2) what are the gene regulatory networks underlying ascidian development? By formalizing simple rules used by researchers to infer gene regulatory events, I was able to automatically recover, from the experimental data available in ANISEED, a network of gene regulatory interactions throughout ascidian development. These simple examples may indicate potential paths towards the use of data integration in Biology.