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BMC Bioinformatics. 2005;6 Suppl 1:S20. Epub 2005 May 24.

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

Author information

1
Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov

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.

PMID:
15960833
PMCID:
PMC1869013
DOI:
10.1186/1471-2105-6-S1-S20
[Indexed for MEDLINE]
Free PMC Article

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