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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| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Online Access: | http://eprints.usm.my/51469/ |
| Abstract | Abstract here |
| _version_ | 1855632743907459072 |
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| author | Ng, Ee Ling |
| author_facet | Ng, Ee Ling |
| author_sort | Ng, Ee Ling |
| description | 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. |
| first_indexed | 2025-10-17T08:37:26Z |
| format | Thesis |
| id | usm-51469 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2025-10-17T08:37:26Z |
| publishDate | 2009 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | usm-514692022-02-09T07:29:26Z http://eprints.usm.my/51469/ Classification Of Microarray Datasets Using Random Forest Ng, Ee Ling QA76.9.D32 Databases 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. 2009-06 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/51469/1/cd%20tesis%20classification%20of%20microarray%20cut.pdf Ng, Ee Ling (2009) Classification Of Microarray Datasets Using Random Forest. Masters thesis, Universiti Sains Malaysia. |
| spellingShingle | QA76.9.D32 Databases Ng, Ee Ling Classification Of Microarray Datasets Using Random Forest |
| thesis_level | Master |
| title | Classification Of Microarray Datasets Using
Random Forest |
| title_full | Classification Of Microarray Datasets Using
Random Forest |
| title_fullStr | Classification Of Microarray Datasets Using
Random Forest |
| title_full_unstemmed | Classification Of Microarray Datasets Using
Random Forest |
| title_short | Classification Of Microarray Datasets Using
Random Forest |
| title_sort | classification of microarray datasets using random forest |
| topic | QA76.9.D32 Databases |
| url | http://eprints.usm.my/51469/ |
| work_keys_str_mv | AT ngeeling classificationofmicroarraydatasetsusingrandomforest |