8) BioWare: A framework for bioinformatics data retrieval, annotation and publishing.
Koh JLY, Krishnan SPT, Seah SH, Tan PT, Khan AM, Lee ML, Brusic V.
27th Annual International ACM SIGIR Conference on Research and Development in IR. Sheffield, UK, July 29, 2004.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 10 (total): 3 (non-self) & 7 (self)
ABSTRACT :
In-depth analysis about a specific subject in molecular biology, specifically those associated with the structural and functional properties of a particular group of sequences typically requires access to an extensive knowledge base. The knowledge base may take the form of a specialist database or subject-specific data warehouse (SSDW) to facilitate the organisation of specialized data and the extraction of new knowledge. These SSDWs are particularly useful for data mining or knowledge discovery processes which require the relevant information from multiple data sources. The construction of a specialist database is a multistep process which typically involves enrichment of annotations (by domain experts), development and integration of analytical tools (by computer programmers), and construction of the system (by database experts). The SSDWs contain focused subsets of data compiled from multiple data sources and enriched with user annotations. In this article we present and describe the BioWare system which enables its users to collect, annotate, publish, and update specialized molecular data in personalized WWW accessible databases. BioWare contains four data warehouse enabling components: (i) BioWare-Retrieve searches and extracts data from selected sources and integrates them into a standardized format, (ii) BioWare-Prep provides a semi-automated mechanism for user-driven cleaning, preliminary analysis and annotation of the data, (iii) TEMPLAR enables users to rapidly create searchable WWW-accessible SSDWs, and (iv) BioWare-Update enables incremental updating of the SSDWs with new data from the sources. We have used BioWare system for the creation and maintenance of several bioinformatic databases.
This article has been cited by other articles:
1) Zhang ZH, Tan SC, Koh JL, Falus A, Brusic V. ALLERDB database and integrated bioinformatic tools for assessment of allergenicity and allergic cross-reactivity.
Cell Immunol. 2006 Dec;244(2):90-6. Epub 2007 Apr 30. PMID: 17467675
2) Zhang ZH, Koh JL, Zhang GL, Choo KH, Tammi MT, Tong JC. AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins. Bioinformatics. 2007 Feb 15;23(4):504-6. Epub 2006 Dec 6. PMID: 17150996
3) Tagger, B. A Framework forManaging Changes in Biological Data. The EngD First Year Report. November 16, 2005. http://www.cs.ucl.ac.uk/staff/btagger/FirstYearRep.pdf
4) Tagger, B. A Framework forManaging Changes in Biological Data. The EngD Second Year Report. October 18, 2006. http://www.cs.ucl.ac.uk/staff/btagger/Second.pdf
5) Tagger, B. A Literature Review for the Problem of Biological Data Versioning. 28th July 2005. http://www.cs.ucl.ac.uk/staff/btagger/LitReview.pdf
6) Tongchusak, S, Chaiyaroj, SC, Veeramani, A, JLY Koh, Brusic, V. CandiVF – Candida albicans Virulence Factor Database. International Journal of Peptide Research and Therapeutics, Vol. 11, No. 4, December 2005, pp. 271–277.
7) Judice, Lie Yong Koh. Correlation-Based Methods for Biological Data Cleaning, with application to biological databases. PhD thesis. 2007, National University of Singapore
8) Lam, K.-T., Koh, J.L.Y., Veeravalli, B., Brusic, V. Incremental maintenance of biological databases using association rule mining. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4146 LNBI, pp. 140-150
9) Miotto, O., Tan, T.W., Brusic, V. Extraction by example: Induction of structural rules for the analysis of molecular sequence data from heterogeneous sources. Lecture Notes in Computer Science. 2005, 3578, pp. 398-405
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