Evaluation of network-guided random forest for disease gene discovery
This work addresses the problem of improving gene selection accuracy in disease prediction for bioinformatics researchers, but it is incremental as it builds on existing random forest methods with network integration.
The study evaluated a network-guided random forest method for disease gene discovery and found it did not improve disease prediction over standard random forest, but identified disease genes more accurately when they formed modules, with empirical analysis on breast cancer datasets showing it could identify genes from related pathways.
Gene network information is believed to be beneficial for disease module and pathway identification, but has not been explicitly utilized in the standard random forest (RF) algorithm for gene expression data analysis. We investigate the performance of a network-guided RF where the network information is summarized into a sampling probability of predictor variables which is further used in the construction of the RF. Our results suggest that network-guided RF does not provide better disease prediction than the standard RF. In terms of disease gene discovery, if disease genes form module(s), network-guided RF identifies them more accurately. In addition, when disease status is independent from genes in the given network, spurious gene selection results can occur when using network information, especially on hub genes. Our empirical analysis on two balanced microarray and RNA-Seq breast cancer datasets from The Cancer Genome Atlas (TCGA) for classification of progesterone receptor (PR) status also demonstrates that network-guided RF can identify genes from PGR-related pathways, which leads to a better connected module of identified genes.