Biomarker discovery for toxicity

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.