Cooperation among cancer cells: applying game theory to cancer

Cell cooperation promotes many of the hallmarks of cancer via the secretion of diffusible factors that can affect cancer cells or stromal cells in the tumour microenvironment. This cooperation cannot be explained simply as the collective action of cells for the benefit of the tumour because non-cooperative subclones can constantly invade and free-ride on the diffusible factors produced by the cooperative cells. A full understanding of cooperation among the cells of a tumour requires methods and concepts from evolutionary game theory, which has been used successfully in other areas of biology to understand similar problems but has been underutilized in cancer research. Game theory can provide insights into the stability of cooperation among cells in a tumour and into the design of potentially evolution-proof therapies that disrupt this cooperation

Simulated annealing least squares twin support vector machine (SA-LSTSVM) for pattern classification

Least squares twin support vector machine (LSTSVM) is a relatively new version of support vector machine (SVM) based on non-parallel twin hyperplanes. Although, LSTSVM is an extremely efficient and fast algorithm for binary classification, its parameters depend on the nature of the problem. Problem dependent parameters make the process of tuning the algorithm with best values for parameters very difficult, which affects the accuracy of the algorithm. Simulated annealing (SA) is a random search technique proposed to find the global minimum of a cost function. It works by emulating the process where a metal slowly cooled so that its structure finally “freezes”. This freezing point happens at a minimum energy configuration. The goal of this paper is to improve the accuracy of the LSTSVMalgorithmby hybridizing it with simulated anneaing. Our research to date suggests that this improvement on the LSTSVM is made for the first time in this paper. Experimental results on several benchmark datasets demonstrate that the accuracy of the proposed algorithm is very promising when compared to other classification methods in the literature. In addition, computational time analysis of the algorithm showed the practicality of the proposed algorithm where the computational time of the algorithm falls between LSTSVM and SVM