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

Jianlin Wang; Xuebing Dai; Huimin Luo; Chaokun Yan; Ge Zhang; Junwei Luo

doi:10.3389/fgene.2021.670232

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

Front Genet. 2021 Jun 3:12:670232. doi: 10.3389/fgene.2021.670232. eCollection 2021.

Authors

Jianlin Wang¹, Xuebing Dai¹, Huimin Luo¹, Chaokun Yan¹, Ge Zhang¹, Junwei Luo²

Affiliations

¹ School of Computer and Information Engineering, Henan University, Kaifeng, China.
² College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.

Abstract

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.

Keywords: DenseNet; RNA-seq data; cancer classification; guided grad-CAM algorithm; pan-cancer.