Data-poor categorization and passage retrieval for gene ontology annotation in Swiss-Prot.
Web of Science
AuthorEhrler, F; Geissbühler, A; Jimeno, A; Ruch, P
Source TitleBMC Bioinformatics
PublisherSpringer Science and Business Media LLC
University of Melbourne Author/sJimeno Yepes, Antonio
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsEhrler, F., Geissbühler, A., Jimeno, A. & Ruch, P. (2005). Data-poor categorization and passage retrieval for gene ontology annotation in Swiss-Prot.. BMC Bioinformatics, 6 Suppl 1 (SUPPL.1), pp.S23-. https://doi.org/10.1186/1471-2105-6-S1-S23.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869016
BACKGROUND: In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignment; 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignment of a set of categories. METHODS: Sentence is the basic retrieval unit. Our classifier computes a distance between each sentence and the GO category provided with the Swiss-Prot entry. The Text Categorizer computes a distance between each GO term and the text of the article. Evaluations are reported both based on annotator judgements as established by the competition and based on mean average precision measures computed using a curated sample of Swiss-Prot. RESULTS: Our system achieved the best recall and precision combination both for passage retrieval and text categorization as evaluated by official evaluators. However, text categorization results were far below those in other data-poor text categorization experiments The top proposed term is relevant in less that 20% of cases, while categorization with other biomedical controlled vocabulary, such as the Medical Subject Headings, we achieved more than 90% precision. We also observe that the scoring methods used in our experiments, based on the retrieval status value of our engines, exhibits effective confidence estimation capabilities. CONCLUSION: From a comparative perspective, the combination of retrieval and natural language processing methods we designed, achieved very competitive performances. Largely data-independent, our systems were no less effective that data-intensive approaches. These results suggests that the overall strategy could benefit a large class of information extraction tasks, especially when training data are missing. However, from a user perspective, results were disappointing. Further investigations are needed to design applicable end-user text mining tools for biologists.
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