The Experts below are selected from a list of 8766 Experts worldwide ranked by ideXlab platform
Isabel Ribagarcia - One of the best experts on this subject based on the ideXlab platform.
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Isabel Ribagarcia, Shabaz MohammedAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository. PERSPECTIVE
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Zhikang Yin, Isabel RibagarciaAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.
Chris F Taylor - One of the best experts on this subject based on the ideXlab platform.
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Isabel Ribagarcia, Shabaz MohammedAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository. PERSPECTIVE
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Zhikang Yin, Isabel RibagarciaAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.
Curtis D Jamison - One of the best experts on this subject based on the ideXlab platform.
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structured query language sql fundamentals
Current protocols in human genetics, 2003Co-Authors: Curtis D JamisonAbstract:Relational databases provide the most common platform for storing data. The structured query language (SQL) is a powerful tool for interacting with relational database systems. SQL enables the user to concoct complex and powerful queries in a straightforward manner, allowing sophisticated data analysis using simple syntax and structure. This unit demonstrates how to use the MySQL package to build and interact with a relational database.
Shabaz Mohammed - One of the best experts on this subject based on the ideXlab platform.
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Isabel Ribagarcia, Shabaz MohammedAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository. PERSPECTIVE
Kevin Garwood - One of the best experts on this subject based on the ideXlab platform.
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Isabel Ribagarcia, Shabaz MohammedAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository. PERSPECTIVE
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a systematic approach to modeling capturing and disseminating proteomics experimental data
Nature Biotechnology, 2003Co-Authors: Chris F Taylor, Norman W Paton, Kevin Garwood, P Kirby, David Stead, Eric W Deutsch, Laura Selway, Janet Walker, Zhikang Yin, Isabel RibagarciaAbstract:Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.
