RFPRED: A RANDOM FOREST APPROACH FOR PREDICTION OF MISSENSE VARIANTS IN HUMAN EXOME
Fabienne JABOT-HANIN, Hugo Varet, Frederic Tores, Alexandre Alcais, Jean-Philippe Jais
RfPred uses random forest to create a meta-score for predicting deleterious missense variants in human exomes, outperforming existing methods and providing pre-calculated scores for all non-synonymous SNPs.
bioRxiv · Jan 20, 2016 · PreprintGenomics