Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis
Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00289/full |
| _version_ | 1811254685264248832 |
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| author | Christine eStaiger Christine eStaiger Sidney eCadot Balázs eGyörffy Lodewyk FA Wessels Lodewyk FA Wessels Gunnar W Klau Gunnar W Klau |
| author_facet | Christine eStaiger Christine eStaiger Sidney eCadot Balázs eGyörffy Lodewyk FA Wessels Lodewyk FA Wessels Gunnar W Klau Gunnar W Klau |
| author_sort | Christine eStaiger |
| collection | DOAJ |
| description | Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al. (2012), PLoS One, 7, 4: e34796), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks. |
| first_indexed | 2024-04-12T17:11:25Z |
| format | Article |
| id | doaj.art-8bf20212deca4942a3070e0c96597fbc |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T17:11:25Z |
| publishDate | 2013-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-8bf20212deca4942a3070e0c96597fbc2022-12-22T03:23:48ZengFrontiers Media S.A.Frontiers in Genetics1664-80212013-12-01410.3389/fgene.2013.0028968713Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosisChristine eStaiger0Christine eStaiger1Sidney eCadot2Balázs eGyörffy3Lodewyk FA Wessels4Lodewyk FA Wessels5Gunnar W Klau6Gunnar W Klau7Centrum Wiskunde & InformaticaThe Netherlands Cancer InstituteThe Netherlands Cancer InstituteHungarian Academy of SciencesThe Netherlands Cancer InstituteTU DelftCentrum Wiskunde & InformaticaVU University AmsterdamIntegrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al. (2012), PLoS One, 7, 4: e34796), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks.http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00289/fullClassificationSoftwarebreast cancernetworksEvaluationFeature Selection |
| spellingShingle | Christine eStaiger Christine eStaiger Sidney eCadot Balázs eGyörffy Lodewyk FA Wessels Lodewyk FA Wessels Gunnar W Klau Gunnar W Klau Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis Frontiers in Genetics Classification Software breast cancer networks Evaluation Feature Selection |
| title | Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis |
| title_full | Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis |
| title_fullStr | Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis |
| title_full_unstemmed | Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis |
| title_short | Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis |
| title_sort | current composite feature classification methods do not outperform simple single genes classifiers in breast cancer prognosis |
| topic | Classification Software breast cancer networks Evaluation Feature Selection |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00289/full |
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