Product Description
Abstract—Feature selection from microarray data has become an ever evolving area of research. Numerous techniques have widely been applied for extraction of genes which are expressed differentially in microarray data. Some of these comprise of studies related to fold-change approach, < Final Year Projects > classical t-statistics and modified t-statistics. It has been found that the gene lists returned by these methods are dissimilar. In this work we compare the outputs of two different feature selection methods using three classifiers based on different algorithms namely the Random Forest Ensemble based method, the Support vector machine (SVM) and the KNN methods, using the prediction accuracy of the test datasets.
Including Packages
Our Specialization
Support Service
Statistical Report

satisfied customers
3,589
Freelance projects
983
sales on Site
11,021
developers
175+