Integration of protein data sources through PO

Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven?t be...

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Main Authors: Sidhu, Amandeep, Dillon, Tharam S., Chang, Elizabeth
Format: Conference Paper
Published: Springer-Verlag 2006
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/20823
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author Sidhu, Amandeep
Dillon, Tharam S.
Chang, Elizabeth
author_facet Sidhu, Amandeep
Dillon, Tharam S.
Chang, Elizabeth
author_sort Sidhu, Amandeep
building Curtin Institutional Repository
collection Online Access
description Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven?t been addressed so far in Protein Informatics. This paper outlines protein data source composition approach based on our existing work of Protein Ontology (PO). The proposed approach enables semi-automatic interoperation among heterogeneous protein data sources. The establishment of semantic interoperation over conceptual framework of PO enables us to get a better insight on how information can be integrated systematically and how queries can be composed. The semantic interoperation between protein data sources is based on semantic relationships between concepts of PO. No other such generalized semantic protein data interoperation framework has been considered so far.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:36:30Z
publishDate 2006
publisher Springer-Verlag
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spelling curtin-20.500.11937-208232018-10-02T05:14:56Z Integration of protein data sources through PO Sidhu, Amandeep Dillon, Tharam S. Chang, Elizabeth ontologies protein data protein data sources ontology data source PO protein ontology databases semantic relationships semantic protein data semantic heterogeneity Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven?t been addressed so far in Protein Informatics. This paper outlines protein data source composition approach based on our existing work of Protein Ontology (PO). The proposed approach enables semi-automatic interoperation among heterogeneous protein data sources. The establishment of semantic interoperation over conceptual framework of PO enables us to get a better insight on how information can be integrated systematically and how queries can be composed. The semantic interoperation between protein data sources is based on semantic relationships between concepts of PO. No other such generalized semantic protein data interoperation framework has been considered so far. 2006 Conference Paper http://hdl.handle.net/20.500.11937/20823 10.1007/11827405_51 Springer-Verlag fulltext
spellingShingle ontologies
protein data
protein data sources
ontology
data source
PO
protein ontology
databases
semantic relationships
semantic protein data
semantic heterogeneity
Sidhu, Amandeep
Dillon, Tharam S.
Chang, Elizabeth
Integration of protein data sources through PO
title Integration of protein data sources through PO
title_full Integration of protein data sources through PO
title_fullStr Integration of protein data sources through PO
title_full_unstemmed Integration of protein data sources through PO
title_short Integration of protein data sources through PO
title_sort integration of protein data sources through po
topic ontologies
protein data
protein data sources
ontology
data source
PO
protein ontology
databases
semantic relationships
semantic protein data
semantic heterogeneity
url http://hdl.handle.net/20.500.11937/20823