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Network and data integration for biomarker signature discovery via network smoothed T-statistics.

Cun Y, Fröhlich H - PLoS ONE (2013)

Bottom Line: We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier.Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways.We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only.

View Article: PubMed Central - PubMed

Affiliation: Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Bonn, Germany.

ABSTRACT
Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN (http://cran.r-project.org).

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Prediction performance of stSVM in comparison to other methods in terms of area under ROC curve (AUC).Breast = GSE11121, Ovarian (TCGA) = GSE25136, Prostate = GSE25136, Prostate (MSKCC) = GSE21032.
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pone-0073074-g002: Prediction performance of stSVM in comparison to other methods in terms of area under ROC curve (AUC).Breast = GSE11121, Ovarian (TCGA) = GSE25136, Prostate = GSE25136, Prostate (MSKCC) = GSE21032.

Mentions: Generally we observed a large variability of prediction performances of most tested algorithms across different datasets, which is in agreement with our previous observations [19]. However, our proposed stSVM approach showed on all of our four gene expression datasets a consistently high prediction performance with respect to the area under ROC curve (AUC, Figure 2) and significantly outperformed several competing methods (Tables S5, S6, S7, S8). Notably on two datasets (breast, prostate dataset 1) the AUC was extremely stable and showed only a very small variance across the cross-validation procedure.


Network and data integration for biomarker signature discovery via network smoothed T-statistics.

Cun Y, Fröhlich H - PLoS ONE (2013)

Prediction performance of stSVM in comparison to other methods in terms of area under ROC curve (AUC).Breast = GSE11121, Ovarian (TCGA) = GSE25136, Prostate = GSE25136, Prostate (MSKCC) = GSE21032.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3760887&req=5

pone-0073074-g002: Prediction performance of stSVM in comparison to other methods in terms of area under ROC curve (AUC).Breast = GSE11121, Ovarian (TCGA) = GSE25136, Prostate = GSE25136, Prostate (MSKCC) = GSE21032.
Mentions: Generally we observed a large variability of prediction performances of most tested algorithms across different datasets, which is in agreement with our previous observations [19]. However, our proposed stSVM approach showed on all of our four gene expression datasets a consistently high prediction performance with respect to the area under ROC curve (AUC, Figure 2) and significantly outperformed several competing methods (Tables S5, S6, S7, S8). Notably on two datasets (breast, prostate dataset 1) the AUC was extremely stable and showed only a very small variance across the cross-validation procedure.

Bottom Line: We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier.Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways.We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only.

View Article: PubMed Central - PubMed

Affiliation: Algorithmic Bioinformatics, Bonn-Aachen International Center for IT, Bonn, Germany.

ABSTRACT
Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN (http://cran.r-project.org).

Show MeSH
Related in: MedlinePlus