Deep-LC: A Novel Deep Learning Method of Identifying Non-Small Cell Lung Cancer-Related Genes - Li, Mo; Meng, Guang xian; Liu, Xiao wei; Ma, Tian; Sun, Ge; He, HongMei (Frontiers in Oncology, 2022)
According to statistics, lung cancer kills 1.8 million people each year and is the main cause of cancer mortality worldwide. Non-small cell lung cancer (NSCLC) accounts for over 85 percent of all lung cancers. Lung cancer running in families demonstrated that the susceptibility and survival of Lung cancer are related to specific genes. Genome-wide association analysis (GWAS) and next generation sequencing have been used to discover genes related NSCLC. However, many studies ignored the intricate interactions information between gene pairs. In the paper, we proposed a novel deep learning method named Deep-LC for predicting NSCLC-related genes. First we built a gene interaction network and used graph convolutional networks (GCN) to extract features of genes and interactions between genes pairs. Then a simple convolutional neural network (CNN) module is used as the decoder to decide whether the gene is related to the disease. Deep-LC is an end-to-end method, and from the evaluation results we can conclude that Deep-LC performs well in mining potential NSCLC-related genes and performs better than existing state-of-art methods.