Biological Effects Pattern Recognition Tool Using Multivariate Statistical Data Reduction

We propose multivariate data reduction, interpretation and pattern matching techniques for classifying and testing genomic, proteomic, and metabonomic profiles against patterns of toxicity. For each type of toxic exposure, these analyses will perform: 1)data normalization and validation; 2) data reduction; 4) class discovery; 5) class prediction. The innovation is in the application of hierarchical clustering and classification methodologies that can produce a list of most diagnostic genes, proteins orbiomarkers for each class or type of exposure. We employ powerful literature mining techniques to relate these diagnostic entities to actual networks and mechanisms of action.