A module-based prediction strategy via unsupervised gene clustering to overcome the drawbacks of traditional gene-based prediction (GBP) models. MBP is portable to any test study as long as partial genes in each module exist in the test study. It provides slightly improved accuracy while is considerably more robust than traditional GBP. The method takes advantage of information from genes sharing similar expression patterns. The results of the current study show that the prediction accuracies of the MBP method are slightly better than those of the GBP method in both within-study and inter-study predictions.
Cooperative Studies Program, VA Maryland Health Care System, Perry Point, MD, USA; Department of Biostatistics, Graduate School of Public Health, Pittsburgh, PA, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Informatics, Precision Therapeutics, Inc. Pittsburgh, PA, USA; Dorothy P. and Richard P. Simmons Center for Interstitial Lung Disease, Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
MBP funding source(s)
Supported by National Institutes of Health (KL2 RR024154-04); University of Pittsburgh (Central Research Development Fund; Competitive Medical Research Fund); Cooperative Studies Program, VA Maryland HCS; Precision Therapeutics Inc., Pittsburgh, PA.