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Integration and exploitation of Big Data for human health
Comparing phenotypes between species potentially provides invaluable insights into the pathobiology and etiology of human disease. Phenotypic characterisation of, for example, mouse and zebrafish mutants can provide information that can be used to prioritise gene lists derived from human genome-wide association studies, allow the dissection of loci involved in copy number-variation lesions, and provide functional validation of disease gene candidates, as well as insights into basic biological processes. The ability to cross the species divide has long been a thorny problem, as human and model organism phenotypes are described using different formal ontologies and conceptual approaches. To address this, we are working to develop a series of ontologies and tools that use those ontologies, allowing the seamless integration of phenotypic data between species. We are now applying semantic approaches to the integration of large public datasets including patient electronic health records, drug effect data and the phenotypes of mutant model organisms. This work is concentrating on the use of this data to develop new therapeutic approaches to human disease, for example through the repositioning of existing drugs.
Integration and exploitation of Big Data for human health
Comparing phenotypes between species potentially provides invaluable insights into the pathobiology and etiology of human disease. Phenotypic characterisation of, for example, mouse and zebrafish mutants can provide information that can be used to prioritise gene lists derived from human genome-wide association studies, allow the dissection of loci involved in copy number-variation lesions, and provide functional validation of disease gene candidates, as well as insights into basic biological processes. The ability to cross the species divide has long been a thorny problem, as human and model organism phenotypes are described using different formal ontologies and conceptual approaches. To address this, we are working to develop a series of ontologies and tools that use those ontologies, allowing the seamless integration of phenotypic data between species. We are now applying semantic approaches to the integration of large public datasets including patient electronic health records, drug effect data and the phenotypes of mutant model organisms. This work is concentrating on the use of this data to develop new therapeutic approaches to human disease, for example through the repositioning of existing drugs.
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论文共 211 篇作者统计合作学者相似作者
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medRxiv (Cold Spring Harbor Laboratory) (2023)
SWAT4HCLSpp.151-152, (2023)
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Mammalian genome : official journal of the International Mammalian Genome Societyno. 3 (2023): 389-407
Karin Slater,Paul N. Schofield, James Wright, Paul Clift, Anushka Irani,William Bradlow,Furqan Aziz,Georgios V Gkoutos
medrxiv(2023)
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Nature Biotechnologyno. 6 (2022): 817-820
Computers in biology and medicine (2022): 106425-106425
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