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An integrative approach for measuring semantic similarities using gene ontology.

Peng J, Li H, Jiang Q, Wang Y, Chen J - BMC Syst Biol (2014)

Bottom Line: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications.The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness.

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ABSTRACT

Background: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.

Results: We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.

Conclusions: InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.

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Comparing InteGO2 with other measures with protein sequence similarity on on human. The x-axis is the BLAST sequence similarity and y-axis is the normalized semantic similarity based on GO.
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Figure 9: Comparing InteGO2 with other measures with protein sequence similarity on on human. The x-axis is the BLAST sequence similarity and y-axis is the normalized semantic similarity based on GO.

Mentions: In addition to use the logFC score as the evaluation criteria, we used protein sequence similarity as an independent evidence for further performance evaluation on the molecular function category [18]. In this experiment, the same human gene set in subsection "Performance evaluation on molecular function" was used, and the sequence similarity scores (ln(BitScore)) were calculated with BLAST [37]. Figure 9 shows that among all the GO based semantic similarity measures, InteGO2 has the highest correlation score with the sequence based similarity with R-Squared 0.96 (polynomial model; Supplementary Table S10 in Additional file 4).


An integrative approach for measuring semantic similarities using gene ontology.

Peng J, Li H, Jiang Q, Wang Y, Chen J - BMC Syst Biol (2014)

Comparing InteGO2 with other measures with protein sequence similarity on on human. The x-axis is the BLAST sequence similarity and y-axis is the normalized semantic similarity based on GO.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4305987&req=5

Figure 9: Comparing InteGO2 with other measures with protein sequence similarity on on human. The x-axis is the BLAST sequence similarity and y-axis is the normalized semantic similarity based on GO.
Mentions: In addition to use the logFC score as the evaluation criteria, we used protein sequence similarity as an independent evidence for further performance evaluation on the molecular function category [18]. In this experiment, the same human gene set in subsection "Performance evaluation on molecular function" was used, and the sequence similarity scores (ln(BitScore)) were calculated with BLAST [37]. Figure 9 shows that among all the GO based semantic similarity measures, InteGO2 has the highest correlation score with the sequence based similarity with R-Squared 0.96 (polynomial model; Supplementary Table S10 in Additional file 4).

Bottom Line: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications.The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.

Results: We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.

Conclusions: InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.

Show MeSH