Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Support vector machine classification on the web
Bioinformatics
Proceedings of the 9th annual conference on Genetic and evolutionary computation
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
Computers and Electronics in Agriculture
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Toxicity biomarkers allow the safe evaluation of possible toxic effects of a substance in early phases of drug discovery. Finding the optimal subset of genes to use as biomarkers is an important problem. We tried evolutionary classification methods for finding biomarkers in hexachlorobenzene (HCB) toxicity using microarray data. We improve upon Kucukural et al. by modifying the algorithm to incrementally filter the features instead of generating new populations from scratch and by finding the common subset of features from multiple runs to be used as biomarkers. Using this modified genetic algorithm, we discovered gene sets of size 4 that were able to predict HCB exposure with 99% accuracy in 5-fold cross-validation tests. Repeating this process on independent test studies resulted in 14 biologically significant genes that predict exposure with 91% accuracy, surpassing other feature selection methods. Making use of these genes as biomarkers may allow us to detect hepatotoxic substances similar to HCB in a fast and cost-efficient manner when there are no emerging symptoms.