Research on Prognostic Risk Assessment Algorithm Based on Graph Neural Networks and Attention Mechanisms

By integrating multi-omics data and assessing medical risk based on patient-specific genes (cancer prognostic risk assessment), prognostic interventions and personalized treatment can be efficiently provided for patients, thus significantly improving the survival rate. In this paper, we propose AGPP (Attention graph prognosis prediction), a method for assessing medical risk based on multi-omics data. In this paper, genes with robust and biological significance were screened in combination with tumor-related knowledge by statistical test, and carried out bioinformatic analysis combined with biological knowledge. Secondly, Secondly, based on the gene set obtained by bioinformatics analysis, a new tumor prognosis risk assessment method is proposed, which combines graph neural networks and attention mechanisms, realizing the stratification of survival risk. The experiment is trained on the multi-omics data and survival data of pancreatic cancer through ten-fold cross-validation. The results indicate that the method takes the best effect, applicable to divide the survival subtypes of pancreatic cancer.