Unification of protein data and knowledge sources
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Advances in technology and the growth of life sciences are generating ever increasing amounts of data. High-throughput techniques are regularly used to capture thousands of data points in an experiment. The results of these experiments normally end up in scientific databases and publications. Although there have been concerted efforts to capture more scientific data in specialist databases, it is generally acknowledged that only 20 per cent of biological knowledge and data is available in a structured format. The remaining 80 per cent of biological information is hidden in the unstructured scientific results and texts. Protein Ontology (PO) discussed in this paper provides a common structured vocabulary for this structured and unstructured information and provides researchers a medium to share knowledge in proteomics domain. It consists of concepts, which are data descriptors for proteomics data and the relations among these concepts. Protein Ontology provides description for protein domains that can be used to describe proteins in any organism.
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Chang, Elizabeth; Sidhu, Amandeep; Dillon, Tharam S. (2005)In this paper, we proposed a Protein Ontology to integrate protein data and information from various Protein Data Sources. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow ...
Sidhu, Amandeep; Dillon, Tharam S.; Chang, Elizabeth (2007)These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches ...
Chang, Elizabeth; Sidhu, Amandeep; Dillon, Tharam S.; Sidhu, B. (2005)These Huge amounts of Protein Structure Data make it difficult to create explanatory and predictive models that are consistent with huge volume of data. Difficulty increase when large variety of heterogeneous approaches ...