A Swarm Intelligence Based Algorithm for Proteomic Pattern Detection of Ovarian Cancer

The advanced protein profiling technologies can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establish signature proteomic patterns that distinguish cancer from non-cancer. It is desirable to have complex and intelligent analytical tools to detect the changes in protein expression and their correlation to diseases conditions. This paper proposed a swarming-agent based intelligence algorithm using a hybrid ant colony optimization/particle swarm optimization (ACO/PSO) algorithm to identify the diagnostic proteomic patterns of biomarkers for early detection of ovarian cancer. The experimental results demonstrated that the proposed system has high predictive accuracy and better diagnostic performance