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2005

Mining protein function from text using term-based support vector machines

8 years 9 months ago
Mining protein function from text using term-based support vector machines
Background: Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results: The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantia...
Simon B. Rice, Goran Nenadic, Benjamin J. Stapley
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where BMCBI
Authors Simon B. Rice, Goran Nenadic, Benjamin J. Stapley
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