Abstract
Early detection of cancer is crucial for successful treatments. In this paper, we propose a multiclass Logistic Partial Least Squares (LPLS) algorithm for classification of normal vs. cancer using Mass Spectrometry (MS). LPLS combines the multiclass logistic regression with Partial Least Squares (PLS) algorithm. Wavelet decomposition is also proposed for pre-processing of original data. Wavelet decomposition and the proposed LPLS are applied to real life cancer data. Experimental comparisons show that LPLS with wavelet decomposition outperforms other methods in the analysis of MS data.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-10 |
| Number of pages | 10 |
| Journal | International Journal of Bioinformatics Research and Applications |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 2008 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Health Informatics
- Clinical Biochemistry
- Health Information Management
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