Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods
Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/380495 |
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| _version_ | 1849305073242341376 |
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| author | Mark Burton Mads Thomassen Qihua Tan Torben A. Kruse |
| author_facet | Mark Burton Mads Thomassen Qihua Tan Torben A. Kruse |
| author_sort | Mark Burton |
| collection | DOAJ |
| description | Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients. |
| format | Article |
| id | doaj-art-08e358c45fe04258a16730b1fad03f5d |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-08e358c45fe04258a16730b1fad03f5d2025-08-20T03:55:33ZengWileyThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/380495380495Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification MethodsMark Burton0Mads Thomassen1Qihua Tan2Torben A. Kruse3Research Unit of Human Genetics, Institute of Clinical Research, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, DenmarkResearch Unit of Human Genetics, Institute of Clinical Research, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, DenmarkResearch Unit of Human Genetics, Institute of Clinical Research, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, DenmarkResearch Unit of Human Genetics, Institute of Clinical Research, University of Southern Denmark, Sdr. Boulevard 29, 5000 Odense C, DenmarkMachine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.http://dx.doi.org/10.1100/2012/380495 |
| spellingShingle | Mark Burton Mads Thomassen Qihua Tan Torben A. Kruse Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods The Scientific World Journal |
| title | Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods |
| title_full | Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods |
| title_fullStr | Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods |
| title_full_unstemmed | Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods |
| title_short | Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods |
| title_sort | gene expression profiles for predicting metastasis in breast cancer a cross study comparison of classification methods |
| url | http://dx.doi.org/10.1100/2012/380495 |
| work_keys_str_mv | AT markburton geneexpressionprofilesforpredictingmetastasisinbreastcanceracrossstudycomparisonofclassificationmethods AT madsthomassen geneexpressionprofilesforpredictingmetastasisinbreastcanceracrossstudycomparisonofclassificationmethods AT qihuatan geneexpressionprofilesforpredictingmetastasisinbreastcanceracrossstudycomparisonofclassificationmethods AT torbenakruse geneexpressionprofilesforpredictingmetastasisinbreastcanceracrossstudycomparisonofclassificationmethods |