Improving the Prediction of Clinical Outcomes from Genomic Data Using Multiresolution Analysis

The prediction of patient's future clinical outcome, such as Alzheimer's and cardiac disease, using only genomic information is an open problem. In cases when genome-wide association studies (GWASs) are able to find strong associations between genomic predictors (e.g., SNPs) and disease, pattern recognition methods may be able to predict the disease well. Furthermore, by using signal processing methods, we can capitalize on latent multivariate interactions of genomic predictors. Such an approach to genomic pattern recognition for prediction of clinical outcomes is investigated in this work. In particular, we show how multiresolution transforms can be applied to genomic data to extract cues of multivariate interactions and, in some cases, improve on the predictive performance of clinical outcomes of standard classification methods. Our results show, for example, that an improvement of about 6 percent increase of the area under the ROC curve can be achieved using multiresolution spaces to train logistic regression to predict late-onset Alzheimer's disease (LOAD) compared to logistic regression applied directly on SNP data.