Mining protein function from text using term-based support vector machines
<p>Abstract</p> <p>Background</p> <p>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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2005-05-01
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| Series: | BMC Bioinformatics |
| _version_ | 1818758036521484288 |
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| author | Rice Simon B Nenadic Goran Stapley Benjamin J |
| author_facet | Rice Simon B Nenadic Goran Stapley Benjamin J |
| author_sort | Rice Simon B |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.</p> <p>Conclusion</p> <p>A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2.</p> |
| first_indexed | 2024-12-18T06:20:27Z |
| format | Article |
| id | doaj.art-c61ddb506bb5414da0b84e8848ca1a55 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T06:20:27Z |
| publishDate | 2005-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c61ddb506bb5414da0b84e8848ca1a552022-12-21T21:18:10ZengBMCBMC Bioinformatics1471-21052005-05-016Suppl 1S2210.1186/1471-2105-6-S1-S22Mining protein function from text using term-based support vector machinesRice Simon BNenadic GoranStapley Benjamin J<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.</p> <p>Conclusion</p> <p>A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2.</p> |
| spellingShingle | Rice Simon B Nenadic Goran Stapley Benjamin J Mining protein function from text using term-based support vector machines BMC Bioinformatics |
| title | Mining protein function from text using term-based support vector machines |
| title_full | Mining protein function from text using term-based support vector machines |
| title_fullStr | Mining protein function from text using term-based support vector machines |
| title_full_unstemmed | Mining protein function from text using term-based support vector machines |
| title_short | Mining protein function from text using term-based support vector machines |
| title_sort | mining protein function from text using term based support vector machines |
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