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.
Recommended Citation
R. Xu et al., "Gene Expression Data for DLBCL Cancer Survival Prediction with a Combination of Machine Learning Technologies," Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/IEMBS.2005.1616559
Meeting Name
IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Sponsor(s)
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Keywords and Phrases
Cancer; Cellular Biophysics; Genetics; Learning (Artificial Intelligence); Medical Computing; Molecular Biophysics; Neural Nets; Particle Swarm Optimisation
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2006
