Abstract
We present, in this paper, two spectral pattern comparison methods for cancer classification using microarray gene expression data. The proposed methods are different from other current classifiers in the ways features are selected and pattern similarities measured. In addition, these spectral methods do not require any data preprocessing which is neccessary for many other classification techniques. Expertimental results using three popular microarray data sets demonstrate the robustness and effectiveness of the spectral pattern classifiers. © 2006 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 2425-2430 |
| Journal | IEEE Transactions on Circuits and Systems I: Regular Papers |
| Volume | 53 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2006 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Research Keywords
- Classification
- Feature selection
- Microarrays
- Spectral distortions
- Vector quantization
Fingerprint
Dive into the research topics of 'Spectral pattern comparison methods for cancer classification based on microarray gene expression data'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver