Metrics for GO based protein semantic similarity: a systematic evaluation
<p>Abstract</p> <p>Background</p> <p>Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic simila...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2008-04-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/S5/S4 |
| _version_ | 1818777247564169216 |
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| author | Falcão André O Ferreira António EN Bastos Hugo Faria Daniel Pesquita Catia Couto Francisco M |
| author_facet | Falcão André O Ferreira António EN Bastos Hugo Faria Daniel Pesquita Catia Couto Francisco M |
| author_sort | Falcão André O |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.</p> <p>Results</p> <p>We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation.</p> <p>Conclusions</p> <p>This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid <it>simGIC</it> was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.</p> |
| first_indexed | 2024-12-18T11:25:48Z |
| format | Article |
| id | doaj.art-baad1e199dd741d082ea95ffb818d15e |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T11:25:48Z |
| publishDate | 2008-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-baad1e199dd741d082ea95ffb818d15e2022-12-21T21:09:42ZengBMCBMC Bioinformatics1471-21052008-04-019Suppl 5S410.1186/1471-2105-9-S5-S4Metrics for GO based protein semantic similarity: a systematic evaluationFalcão André OFerreira António ENBastos HugoFaria DanielPesquita CatiaCouto Francisco M<p>Abstract</p> <p>Background</p> <p>Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.</p> <p>Results</p> <p>We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation.</p> <p>Conclusions</p> <p>This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid <it>simGIC</it> was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.</p>http://www.biomedcentral.com/1471-2105/9/S5/S4 |
| spellingShingle | Falcão André O Ferreira António EN Bastos Hugo Faria Daniel Pesquita Catia Couto Francisco M Metrics for GO based protein semantic similarity: a systematic evaluation BMC Bioinformatics |
| title | Metrics for GO based protein semantic similarity: a systematic evaluation |
| title_full | Metrics for GO based protein semantic similarity: a systematic evaluation |
| title_fullStr | Metrics for GO based protein semantic similarity: a systematic evaluation |
| title_full_unstemmed | Metrics for GO based protein semantic similarity: a systematic evaluation |
| title_short | Metrics for GO based protein semantic similarity: a systematic evaluation |
| title_sort | metrics for go based protein semantic similarity a systematic evaluation |
| url | http://www.biomedcentral.com/1471-2105/9/S5/S4 |
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