The nature of statistical learning theory
The nature of statistical learning theory
NetAffx: affymetrix probeset annotations
Proceedings of the 2002 ACM symposium on Applied computing
ADHOC: a Tool for Performing Effective Feature Selection
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support vector machine classification on the web
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the 9th annual conference on Genetic and evolutionary computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Biomarker discovery for toxicity
Neurocomputing
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Discovering toxicity biomarkers is important in drug discovery to safely evaluate possible toxic effects of a substance in early phases. We tried evolutionary classification methods for selecting the important classifier genes in hexachlorobenzene toxicity using microarray data. Using modified genetic algorithms for selection of minimum number of features for classification of gene expression data, we discovered a number of gene sets of size 4 that were able to discriminate between the control and the hexachlorobenzene (HCB) exposed group of Brown-Norway rats with 99% accuracy in 5-fold cross-validation tests, whereas classification using all of the genes with SVM and other methods yielded results that vary between 48.48% to 81.81%. Making use of this small number of genes as biomarkers may allow us to detect toxicity of substances with mechanisms of toxicity similar to HCB in a fast and cost efficient manner when there are no emerging symptoms.