Computational method for discovery of biomarker signatures from large, complex data sets

We present an efficient method for identifying of reliable biomarker panels from large multivariate data sets that typically result from experiments that monitor changes in RNA, small molecule, or protein abundance. Our computational methodology is developed and validated on the toxicogenomics database Drug Matrix that in its largest category contains 1656 recognition targets, characterized by the toxicant, dose and time (or duration) of the exposure. We were able to recognize both individual experimental conditions (compound, dose and time combinations) and the cases where the values for dose and time variables fall within the intervals in the training data, but do not match the training data exactly. Inclusion of gene expression information for multiple organs improved accuracy of recognition. Inclusion of time response information into consideration allowed us to develop particularly accurate marker panels for a large number of targets: we were able to recognize 176 compounds (out of 316) at greater than 90% accuracy. The presented methodology has an immediate application for discovery of diagnostic biomarker panels for exposure to various toxicity hazards, and may also be useful for development of biological markers for medical applications.