Effects of Random Forest Parameters in the Selection of Biomarkers

A microarray dataset contains thousands of DNA spots covering almost every gene in the genome. Microarray-based gene expression helps with the diagnosis, prognosis and treatment of cancer. The nature of diseases frequently changes, which in turn generates a considerable volume of data. The main drawback of microarray data is the curse of dimensionality. It hinders useful information and leads to computational instability. The main objective of feature selection is to extract and remove insignificant and irrelevant features to determine the informative genes that cause cancer. Random forest is a well-suited classification algorithm for microarray data. To enhance the importance of the variables, we proposed out-of-bag (OOB) cases in every tree of the forest to count the number of votes for the exact class. The incorporation of random permutation in the variables of these OOB cases enables us to select the crucial features from high-dimensional microarray data. In this study, we analyze the effects of various random forest parameters on the selection procedure. 'Variable drop fraction' regulates the forest construction. The higher variable drop fraction value efficiently decreases the dimensionality of the microarray data. Forest built with 800 trees chooses fewer important features under any variable drop fraction value that reduces microarray data dimensionality.

Automated summary

The microarray dataset covers almost every gene in the genome and helps with cancer diagnosis, prognosis, and treatment. The curse of dimensionality in microarray data hinders useful information and leads to computational instability. Feature selection and the random forest algorithm play a crucial role in extracting important features and reducing data dimensionality.