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Integrating Omics Data With Phenotype And Disease Using Ontologies | 9373
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Integrating omics data with phenotype and disease using ontologies

International Conference on Integrative Biology Summit

John M. Hancock

Accepted Abstracts: J Comput Sci Syst Biol

DOI: 10.4172/0974-7230.S1.004

Abstract
W ith the explosion in genome and exome sequencing and other High Throughput Sequencing applications attention is increasingly turning to the interpretation of large volumes of sequence data in a wide variety of contexts. Key data types that need to be integrated with sequence and other omics data to make this possible are phenotype and disease data. This is becoming increasingly important with the advent of the International Mouse Phenotyping Consortium and other high- throughput phenotyping projects using model organisms. Phenotype and disease data have historically suffered from a lack of appropriate forms of data representation for computational analysis but this is changing. I will review the current and developing formalisms for representing phenotype and disease and approaches for integrating them with omics data and their use in building heterogeneous data networks.
Biography
W ith the explosion in genome and exome sequencing and other High Throughput Sequencing applications attention is increasingly turning to the interpretation of large volumes of sequence data in a wide variety of contexts. Key data types that need to be integrated with sequence and other omics data to make this possible are phenotype and disease data. This is becoming increasingly important with the advent of the International Mouse Phenotyping Consortium and other high- throughput phenotyping projects using model organisms. Phenotype and disease data have historically suffered from a lack of appropriate forms of data representation for computational analysis but this is changing. I will review the current and developing formalisms for representing phenotype and disease and approaches for integrating them with omics data and their use in building heterogeneous data networks.
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