Finding A Disease-Related Gene From Microarray Data Using Random Forest

Numerous databases of DNA-microarrays are now widely available on the internet. Recently, there has been increasing interest in the analysis of microarray data using machine-learning techniques due to the amount of data, which is too massive for researchers to analyze using conventional techniques. In this study, we propose a method of finding a disease-related gene from microarray data using random forest, a machine-learning technique. More specifically, we focused on Alzheimer's disease and used microarray data related to Alzheimer's disease in the experiments. In the result, we found some genes that are believed to be related to Alzheimer's disease. Some genes discovered in the result have been investigated for their relevance to Alzheimer's disease, and this proves that our proposed methodology was successful in finding disease-related genes using microarray data. In addition, the proposed methodology is useful in providing new knowledge for biologists, medical scientists, and cognitive computing researchers since there is no previous work on genes that focused on finding a disease-related gene for Alzheimer's disease.