Abstract
Molecular classification of tumors holds great potential for cancer research, diagnosis, and treatment. In this study, we apply a novel classification technique to cDNA microarray data for discriminating between three subtypes of malignant lymphoma: CD5+ diffuse large B-cell lymphoma, CD5- diffuse large B-cell lymphoma, and mantle cell lymphoma. The proposed technique combines the k -Nearest Neighbor (k -NN) algorithm with optimized data quantization. The feature genes on which the classification is based are selected by ranking them according to their separability criteria computed by taking into account between-class and within-class scatter. The classification errors, estimated using cross-validation, are significantly lower than those produced by classical variants of the k -NN algorithm. Multidimensional scaling and hierarchical clustering dendrograms are used to visualize the separation of the three subtypes of lymphoma.
Original language | English (US) |
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Pages (from-to) | 6-17 |
Number of pages | 12 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5328 |
DOIs | |
State | Published - 2004 |
Event | Microarrays and Combinatorial Techniques: Design, Fabrication, and Analysis II - San Jose, CA, United States Duration: Jan 25 2004 → Jan 26 2004 |
Keywords
- Dlbcl mcl classification
- Gene expression
- K-nearest neighbor
- Lymphoma
- Quantization
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering