Meta-analysis of gene expression for development and validation of a diagnostic biomarker panel for Oral Squamous Cell Carcinoma.

We use a newly developed feature extraction and classification method to analyze previously published gene expression data sets in Oral Squamous Cell Carcinoma and in healthy oral mucosa in order to find a gene set sufficient for diagnoses. The feature selection technology is based on the relative dichotomy power concept published by us earlier. The resulting biomarker panel has 100% sensitivity and 95% specificity, is enriched in genes associated with oncogenesis and invasive tumor growth, and, unlike marker panels devised in earlier studies, shows concordance with previously published marker genes.

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.