Abstract
It is crucial for cancer diagnosis and treatment to accurately identify the site of origin of a tumor. with the emergence and rapid advancement of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to research on binary classification such as normal versus tumor samples, which attracts numerous efforts from a variety of disciplines, the discrimination of multiple tumor types is also important. Meanwhile, the selection of genes which are relevant to a certain cancer type not only improves the performance of the classifiers, but also provides molecular insights for treatment and drug development. Here, we use Semisupervised Ellipsoid ARTMAP (ssEAM) for multiclass cancer discrimination and particle swarm optimization for informative gene selection. ssEAM is a neural network architecture rooted in Adaptive Resonance Theory and suitable for classification tasks. ssEAM features fast, stable, and finite learning and creates hyperellipsoidal clusters, inducing complex nonlinear decision boundaries. PSO is an evolutionary algorithm-based technique for global optimization. A discrete binary version of PSO is employed to indicate whether genes are chosen or not. The effectiveness of ssEAM/PSO for multiclass cancer diagnosis is demonstrated by testing it on three publicly available multiple-class cancer data sets. ssEAM/PSO achieves competitive performance on all these data sets, with results comparable to or better than those obtained by other classifiers.
Recommended Citation
G. C. Anagnostopoulos et al., "Multiclass Cancer Classification Using Semisupervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers (IEEE), Jan 2007.
The definitive version is available at https://doi.org/10.1109/TCBB.2007.1009
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 Classification; Gene Explosion Profiles; Particle Swarm Optimization; Semisupervised Ellipsoid ARTMAP
International Standard Serial Number (ISSN)
1545-5963
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2007
