A Robust Method For The Interpretation Of Genomic Data

This paper presents a robust methodology to find biomarkers that are predictive of any given clinical outcome, by combining three critical steps: Adjustment for correlated biomarkers, through Linkage Disequilibrium pre-processing; False Detection Rate (FD) control with q-values; multivariate predictive modelling with neural networks. The results show that neural network modelling with pre-processing using p-values can be misleading. In particular, the interpretation of the neural network through calculation of the conditional probabilities P(x vertical bar c) where x represents covariates and c the classes, haw an important role in elucidating the predictive power (or lack of it) of the biomarkers. The methodology is generally applicable to p>n modelling where the initial pool of potential predictive parameters p, e.g. biomarkers, is greater than the sample size n