Providing An Optimized Model to Detect Driver Genes From Heterogeneous Cancer Samples, Using Restriction in Subspace Learning

Abstract

Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more efficient for more genes compared to other methods. The p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. The results show that the p. value of the proposed method is about 2.7 times less than the driver sub method, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.