Class Prediction Methods Applied to Microarray Data for Classification - Shukir, Fatima (Iraqi Journal of Science, 2024)
The use of microarray data for the analysis of gene expression has been seen tobe an important tool in biological research over the last decade. The important roleof 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 whichgenes play an important role in the prediction of class membership? There are manyclassification methods applied to microarray data to solve the classification problem.In bioinformatics, Statistical method is addressed by using microarray data. Forexample breast tissue samples could be classified as either cancerous or normal.Microarray expression profiling has provided an exciting new technology to identifyclassifiers for selection treatments to patients. Sometime in special cases, prognosticprediction is included in class prediction. In order to predict which patient willrespond to a specified treatment we can think about two classes, includingresponders and no responders. The objective may be to predict whether a newpatient is likely to respond based on the Microarray expression profile of her or histissue sample. That it is mean accurate prediction is of obvious value in treatmentselection. To achieve the above objectives I used many methods for class predictionusing gene expression profiles from microarray experiments. This research aims toexplain what these methods are, how these methods are applied to the microarraydataset, analyzes the results and how feature selection is used for classification.Furthermore, comparison of these methods and cross validation will be used toevaluate the predictive accuracy.