Gene Expression Data for DLBCL Cancer Survival Prediction with a Combination of Machine Learning Technologies
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1646
There were 7 downloads as of 28 Jun 2016.
Abstract
Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expression data analysis, the curse of dimensionality, in order to discriminate high risk patients from low risk patients. A discrete binary version of PSO is used for gene selection and dimensionality reduction, and a probabilistic neural network (PNN) is implemented as the classifier. The experimental results on the diffuse large B-cell lymphoma data set demonstrate the effectiveness of PSO/PNN system in survival prediction.
