Evidence accumulation to identify discriminatory signatures in biomedical spectra

Authors:
A. Bamgbade;R. Somorjai;B. Dolenko;E. Pranckeviciene;A. Nikulin;R. Baumgartner
Affiliations:
Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada
Venue:
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Year:
2005

Citing 3
Cited 0

Consensus-Based Identification of Spectral Signatures for Classification of High-Dimensional Biomedical Spectra

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Filter versus wrapper gene selection approaches in DNA microarray domains

Artificial Intelligence in Medicine

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Abstract

Extraction of meaningful spectral signatures (sets of features) from high-dimensional biomedical datasets is an important stage of biomarker discovery. We present a novel feature extraction algorithm for supervised classification, based on the evidence accumulation framework, originally proposed by Fred and Jain for unsupervised clustering. By taking advantage of the randomness in genetic-algorithm-based feature extraction, we generate interpretable spectral signatures, which serve as hypotheses for corroboration by further research. As a benchmark, we used the state-of-the-art support vector machine classifier. Using external crossvalidation, we were able to obtain candidate biomarkers without sacrificing prediction accuracy.