MI_DenseNetCAM: A Novel Pan-Cancer Classification and Prediction Method Based on Mutual Information and Deep Learning Model

• Published in: Frontiers in genetics Vol. 12; p. 670232
• Main Authors: Wang, Jianlin, Dai, Xuebing, Luo, Huimin, Yan, Chaokun, Zhang, Ge, Luo, Junwei
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 03.06.2021
• Subjects: cancer classification; DenseNet; Genetics; guided grad-CAM algorithm; pan-cancer; RNA-seq data
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.670232

The Pan-Cancer Atlas consists of original sequencing data from various sources, provides the opportunity to perform systematic studies on the commonalities and differences between diverse cancers. The analysis for the pan-cancer dataset could help researchers to identify the key factors that could trigger cancer. In this paper, we present a novel pan-cancer classification method, referred to MI_DenseNetCAM, to identify a set of genes that can differentiate all tumor types accurately. First, the Mutual Information (MI) was utilized to eliminate noise and redundancy from the pan-cancer datasets. Then, the gene data was further converted to 2D images. Next, the DenseNet model was adopted as a classifier and the Guided Grad-CAM algorithm was applied to identify the key genes. Extensive experimental results on the public RNA-seq data sets with 33 different tumor types show that our method outperforms the other state-of-the-art classification methods. Moreover, gene analysis further demonstrated that the genes selected by our method were related to the corresponding tumor types.