A Model for Detection of brain cancer sub-types based on deep random forest and augmented features using genomic data

Diagnosing the type of cancer, which is called the subtype, is very important in determining the treatment process. This paper focuses on the diagnose of the four subtypes of the brain cancer. Disease subtype diagnosis can be modeled as a classification problem. Due to the significant progress made in bioinformatics in extracting genetic information from the human body, recently this information is widely used in the representing of patients in machine learning. In this paper, three types of genetic information including mRNA, miRNA and DNA methylation are used.It should be noted that combining different information sources in the form of multimodal data instead of using a single information source increases the accuracy of information classification. To extract more desirable features from the original genetic data, auto-encoder has been used so that the features extracted from auto-encoder are concatenated to the original genetic data.Random forest has performed well as a classifier in classifying patients based on genetic information. By extending deep methods in neural networks and their good performance, a version of deep random forest with layered structure has been proposed. The deep random forest has the advantage that has a limited number of parameters and lower computational complexity in addition to the optimal performance in information classification. In this paper, deep random forest is used to determine the subtype of a special type of brain cancer. The experiment results show the desired performance of the proposed method.