Classification Of Microarray Datasets Using Random Forest
DNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model...
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| フォーマット: | 学位論文 |
| 言語: | 英語 |
| 出版事項: |
2009
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| 主題: | |
| オンライン･アクセス: | http://eprints.usm.my/51469/ |
| Abstract | Abstract here |
| 要約: | DNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model can be used to predict unknown disease classes in a test case. Prior to a well built model is to achieve good classification results which rely very much on the classifiers that are being used. However, in most microarray data, the number of genes usually outnumbers the number of samples. |
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