Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data
In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testin...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2007-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/117693510700300010 |
| _version_ | 1811286534958088192 |
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| author | Simin Hu J. Sunil Rao |
| author_facet | Simin Hu J. Sunil Rao |
| author_sort | Simin Hu |
| collection | DOAJ |
| description | In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance. |
| first_indexed | 2024-04-13T03:01:42Z |
| format | Article |
| id | doaj.art-75e3a94fb6a7470bbf4aaf7826c5ebb7 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-04-13T03:01:42Z |
| publishDate | 2007-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-75e3a94fb6a7470bbf4aaf7826c5ebb72022-12-22T03:05:24ZengSAGE PublishingCancer Informatics1176-93512007-01-01310.1177/117693510700300010Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray DataSimin Hu0J. Sunil Rao1Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, 44106.Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, 44106.In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.https://doi.org/10.1177/117693510700300010 |
| spellingShingle | Simin Hu J. Sunil Rao Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data Cancer Informatics |
| title | Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data |
| title_full | Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data |
| title_fullStr | Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data |
| title_full_unstemmed | Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data |
| title_short | Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data |
| title_sort | statistical redundancy testing for improved gene selection in cancer classification using microarray data |
| url | https://doi.org/10.1177/117693510700300010 |
| work_keys_str_mv | AT siminhu statisticalredundancytestingforimprovedgeneselectionincancerclassificationusingmicroarraydata AT jsunilrao statisticalredundancytestingforimprovedgeneselectionincancerclassificationusingmicroarraydata |