Predicting non-small cell lung cancer-related genes by a new network-based machine learning method - Cai, Yong; Wu, Qiongya; Chen, Yun; Liu, Yu; Wang, Jiying (Frontiers in Oncology, 2022)
Lung cancer is the leading cause of cancer death globally, killing 1.8 million people year. Over 85% of lung cancer cases are non-small cell lung cancer (NSCLC). Lung cancer running in families has shown that some genes are linked to lung cancer. Genes associated with NSCLC have been found by next-generation sequencing (NGS) and genome-wide association analysis (GWAS). Many papers, however, neglected the complex information about interactions between gene pairs. And along with its high cost, GWAS analysis has an obvious drawback of false-positive results. Based on the above problem, computational techniques are used to offer the researchers alternative and complementary low-cost disease-gene association findings. To help find NSCLC-related genes, we proposed a new network-based machine learning method, named deepRW, to predict genes linked to NSCLC. We first constructed a gene interaction network consisting of genes that related and irrevelant to NSCLC disease, and using deep walk and Graph convolution network (GCN) method to learn gene-disease interactions. Finally, deep neural network (DNN) was utilized as the prediction module to decide which genes is related to NSCLC. To evaluate the performance of deepRW, we ran tests with 10-fold cross validation. The experimental results showed that our method greatly exceeded the existing methods. And the effectiveness of each module in deepRW was demonstrated in comparative experiments.