Accomplishments and challenges of protein ontology

Sidhu, Amandeep and Dillon, Tharam and Hussain, Farookh Khadeer and Chang, Elizabeth. 2006. Accomplishments and challenges of protein ontology, in Ceballos, S. (ed), 30th Annual International Computer Software and Applications Conference (COMPSAC), Sep 17 2006, pp. 71-76. Chicago, USA: IEEE Computer Society.

Source Title

Proceedings of the 30th Annual International Computer Software and Applications Conference (COMPSAC'06)

Source Conference

IEEE International Computer Software and Applications Conference (COMPSAC)

DOI

10.1109/COMPSAC.2006.18

ISBN

0769526551

Faculty

Curtin Business School

Centre for Extended Enterprises and Business Intelligence

School of Information Systems

Remarks

Copyright © 2006 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

URI

http://hdl.handle.net/20.500.11937/13261

Collection

Curtin Research Publications

Abstract

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from unique perspective that is, examining protein data sources through structure and hierarchy of Protein Ontology (PO). Various data mining algorithms and mathematical models provide methods for analysing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO Data of Human Proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO.