Inferring Cell-type-specific Genes of Lung Cancer Based on Deep Learning

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% of all lung cancers. Lung cancer has a strong genetic predisposition, demonstrating that the susceptibility and survival of lung cancer are related to specific genes. Genome-wide association studies (GWASs) and next-generation sequencing have been used to discover genes related to NSCLC. However, many studies ignored the intricate interaction 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 (GCNs) to extract features of genes and interactions between gene 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-the-art methods.

Section: Introductionmentioning

confidence: 99%

Deep-LC: A Novel Deep Learning Method of Identifying Non-Small Cell Lung Cancer-Related Genes

Meng

Liu

et al. 2022

“…Its sample size is insufficient and there is a sample-specific bias. It has become a trend to predict disease-related features through associations between biomolecules ( 16 , 17 ). Therefore, we intend to identify esophageal cancer-related genes by their associations and correlation signatures.…”

Section: Introductionmentioning

confidence: 99%

A computational method for large-scale identification of esophageal cancer-related genes

Qiu

et al. 2022

The incidence of esophageal cancer has obvious genetic susceptibility. Identifying esophageal cancer-related genes plays a huge role in the prevention and treatment of esophageal cancer. Through various sequencing methods, researchers have found only a small number of genes associated with esophageal cancer. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. This method fuses graph convolutional network and logical matrix factorization to effectively identify esophageal cancer-related genes through the association between genes. We call this method GCNLMF which achieved AUC as 0.927 and AUPR as 0.86. Compared with other five methods, GCNLMF performed best. We conducted a case study of the top three predicted genes. Although the association of these three genes with esophageal cancer has not been reported in the database, studies by other reseachers have shown that these three genes are significantly associated with esophageal cancer, which illustrates the accuracy of the prediction results of GCNLMF.

“…With the explosive growth of relevant information and data in recent years, GWAS and other methods become more and more time-consuming and laborious. Many studies have focused on drug-disease association tasks and other bioinformatics tasks through machine learning and deep learning methods (9)(10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Predicting non-small cell lung cancer-related genes by a new network-based machine learning method

Cai

Chen

et al. 2022

Lung cancer is the leading cause of cancer death globally, killing 1.8 million people yearly. 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 studies (GWAS). Many papers, however, neglected the complex information about interactions between gene pairs. 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 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 are related and irrelevant to NSCLC disease and used deep walk and graph convolutional network (GCN) method to learn gene–disease interactions. Finally, deep neural network (DNN) was utilized as the prediction module to decide which genes are 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. In addition, the effectiveness of each module in deepRW was demonstrated in comparative experiments.