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

@article{Verspoor2005, 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.}, address = {Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov}, author = {Verspoor, K. and Cohn, J. and Joslyn, C. and Mniszewski, S. and Rechtsteiner, A. and Rocha, L. M. and Simas, T. }, citeulike-article-id = {594836}, doi = {http://dx.doi.org/10.1186/1471-2105-6-S1-S20}, issn = {1471-2105}, journal = {BMC Bioinformatics}, posted-at = {2008-05-20 21:26:14}, priority = {2}, title = {Protein annotation as term categorization in the gene ontology using word proximity networks.}, url = {http://dx.doi.org/10.1186/1471-2105-6-S1-S20}, volume = {6 Suppl 1}, year = {2005} }

TY - JOUR ID - Verspoor2005 L3 - citeulike-article-id:594836 TI - Protein annotation as term categorization in the gene ontology using word proximity networks. JF - BMC Bioinformatics VL - 6 Suppl 1 AD - Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov SN - 1471-2105 N2 - 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. AU - Verspoor, K AU - Cohn, J AU - Joslyn, C AU - Mniszewski, S AU - Rechtsteiner, A AU - Rocha, LM AU - Simas, T PY - 2005/// UR - http://dx.doi.org/10.1186/1471-2105-6-S1-S20 ER -