Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree

Frontiers in Molecular Biosciences

Jie Zhou¹  Qing Chen²  Ji Zhang²  Banghe Bao³  Fan Zhang⁴ 

[1] Department of Biochemistry and Molecular Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Wuhan Asia General Hospital, Wuhan, China;

关键词: gastric cancer;    susceptibility gene;    gradient boosting decision tree (GBDT);    random walk (RW);    gastric cancer-related genes;   

DOI  :  10.3389/fmolb.2021.815243

来源: DOAJ

【 摘 要 】

The early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant markers for diagnosis and treatment to improve diagnosis precision and guide personalized treatment. In order to further reveal the pathogenesis of gastric cancer at the gene level, we proposed a method based on Gradient Boosting Decision Tree (GBDT) to identify the susceptible genes of gastric cancer through gene interaction network. Based on the known genes related to gastric cancer, we collected more genes which can interact with them and constructed a gene interaction network. Random Walk was used to extract network association of each gene and we used GBDT to identify the gastric cancer-related genes. To verify the AUC and AUPR of our algorithm, we implemented 10-fold cross-validation. GBDT achieved AUC as 0.89 and AUPR as 0.81. We selected four other methods to compare with GBDT and found GBDT performed best.

【 授权许可】

Unknown