{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:23:14Z","timestamp":1767374594833},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"S1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2005,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-6-s1-s20","type":"journal-article","created":{"date-parts":[[2005,5,24]],"date-time":"2005-05-24T18:13:44Z","timestamp":1116958424000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Protein annotation as term categorization in the gene ontology using word proximity networks"],"prefix":"10.1186","volume":"6","author":[{"given":"Karin","family":"Verspoor","sequence":"first","affiliation":[]},{"given":"Judith","family":"Cohn","sequence":"additional","affiliation":[]},{"given":"Cliff","family":"Joslyn","sequence":"additional","affiliation":[]},{"given":"Sue","family":"Mniszewski","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Rechtsteiner","sequence":"additional","affiliation":[]},{"given":"Luis M","family":"Rocha","sequence":"additional","affiliation":[]},{"given":"Tiago","family":"Simas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2005,5,24]]},"reference":[{"issue":"1","key":"655_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"The Gene Ontology Cosortium","year":"2000","unstructured":"The Gene Ontology Cosortium: Gene Ontology: Tool For the Unification of Biology. 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