Immune gene prognostic signature for disease free survival of gastric cancer Translational research of an artificial intelligence survival predictive system

Abstract

Background The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer. Methods Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system. Results Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score. Conclusions The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

“…23 Technical advances in statistics and artificial intelligence (AI) enable computer engineers and health scientists to work closely to improve the prognosis using multifactorial analysis, conventional logistic regression, and Cox analysis. 21,24,25 The accuracy of such predictions is significantly higher than the experimental predictions. In addition, research shows that traditional statistical methods do not provide as accurate analyzes as AI.…”

“…These methods currently play an important role in increasing the accuracy of predicting cancer vulnerability, recurrence, and survival. 19,24,26 Machine learning (ML), as a special concept, is a subset of AI, increasingly used in medicine. This technique is used to build predictive models to extract hidden patterns and uncover unknown correlations from massive historical data.…”

“…These methods currently play an important role in increasing the accuracy of predicting cancer vulnerability, recurrence, and survival. 19,24,26…”