Class Prediction Methods Applied to Microarray Data for Classification | ||
| Iraqi Journal of Science | ||
| Article 1, Volume 53, Issue 5, December 2012, Pages 1193-1206 | ||
| Author | ||
| Fatima .S. Shukir | ||
| Abstract | ||
| The use of microarray data for the analysis of gene expression has been seen to be an important tool in biological research over the last decade. The important role of this tool is indicated by providing patients a great benefit of predicted treatment. There is an important question about a classification problem. The question is which genes play an important role in the prediction of class membership? There are many classification methods applied to microarray data to solve the classification problem. In bioinformatics, Statistical method is addressed by using microarray data. For example breast tissue samples could be classified as either cancerous or normal. Microarray expression profiling has provided an exciting new technology to identify classifiers for selection treatments to patients. Sometime in special cases, prognostic prediction is included in class prediction. In order to predict which patient will respond to a specified treatment we can think about two classes, including responders and no responders. The objective may be to predict whether a new patient is likely to respond based on the Microarray expression profile of her or his tissue sample. That it is mean accurate prediction is of obvious value in treatment selection. To achieve the above objectives I used many methods for class prediction using gene expression profiles from microarray experiments. This research aims to explain what these methods are, how these methods are applied to the microarray dataset, analyzes the results and how feature selection is used for classification. Furthermore, comparison of these methods and cross validation will be used to evaluate the predictive accuracy. | ||
| Keywords | ||
| cancer; microarray data; Classification; cross validation; predictive accuracy; gene expression profiles; Feature selection | ||
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