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This article is part of the supplement: A critical assessment of text mining methods in molecular biology

Open Access Report

Protein annotation as term categorization in the gene ontology using word proximity networks

Karin Verspoor1*, Judith Cohn1, Cliff Joslyn1, Sue Mniszewski1, Andreas Rechtsteiner1, Luis M Rocha23 and Tiago Simas3

Author Affiliations

1 Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA

2 School of Informatics, Indiana University, 1900 East Tenth Street, Bloomington IN 47406, USA

3 Cognitive Science Program, Sycamore Hall 0014, 1033 E. Third Street, Indiana University, Bloomington, IN 47405, USA

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BMC Bioinformatics 2005, 6(Suppl 1):S20  doi:10.1186/1471-2105-6-S1-S20

Published: 24 May 2005

Abstract

Background

We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a protein's document neighborhood into the GO.

Results

The evaluation results indicate that the method for expanding words associated with GO nodes is quite powerful; we were able to successfully select appropriate evidence text for a given annotation in 38% of Task 2.1 queries by building on this method. The term categorization methodology achieved a precision of 16% for annotation within the correct extended family in Task 2.2, though we show through subsequent analysis that this can be improved with a different parameter setting. Our architecture proved not to be very successful on the evidence text component of the task, in the configuration used to generate the submitted results.

Conclusion

The initial results show promise for both of the methods we explored, and we are planning to integrate the methods more closely to achieve better results overall.