Gene reduction for cancer detection using layer-wise relevance propagation

Precise detection of cancer types and normal tissues is crucial for cancer diagnosis. Specifically, cancer classification using gene expression data is key to identify genes whose expression patterns are tumor-specific. Here we aim to search for a minimal set of genes that may reduce the expression complexity and retain a qualified classification accuracy accordingly. We applied neural network models with layer-wise relevance propagation (LRP) to find genes that significantly contribute to classification. Two algorithms for the LRP-candidate gene selection and the cycle of gene reduction were proposed. By implementing the two algorithms for gene reduction, our model retained 95.32% validation accuracy to make classification of six cancer types and normal with a minimal set of seven genes. Furthermore, a cross-evaluation process was performed on the minimal set of seven genes, indicating that the selected marker genes in five out of six cancer types are biologically relevant to cancer annotated by the COSMIC Cancer Gene Census.