Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

Abstract

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values. The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. dargenio@bmsr.usc.edu Supplementary material is available at Bioinformatics online.