Gene reduction for cancer detection using layer-wise relevance propagation
Journal of Decision Making and Healthcare, Volume 1, Issue 1, June 2024, Pages: 30–44
SHENG-YI HSU
Ever Fortune.AI Co., Ltd., Taichung 40360, Taiwan
MAU-HSIANG SHIH
Research Center for Interneural Computing, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
WU-HSIUNG WU
Research Center for Interneural Computing, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
HAO-REN YAO
Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
FENG-SHENG TSAI
Research Center for Interneural Computing, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
Department of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
Abstract
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.
Cite this Article as
Sheng-Yi Hsu, Mau-Hsiang Shih, Wu-Hsiung Wu, Hao-Ren Yao and Feng-Sheng Tsai, Gene reduction for cancer detection using layer-wise relevance propagation, Journal of Decision Making and Healthcare, 1 (2024), 30–44