TY - JOUR
AU - Pal, Ranadip
AB - SummaryIntegratedMRF is an open-source R implementation for integrating drug response predictions from various genomic characterizations using univariate or multivariate random forests that includes various options for error estimation techniques. The integrated framework was developed following superior performance of random forest based methods in NCI-DREAM drug sensitivity prediction challenge. The computational framework can be applied to estimate mean and confidence interval of drug response prediction errors based on ensemble approaches with various combinations of genetic and epigenetic characterizations as inputs. The multivariate random forest implementation included in the package incorporates the correlations between output responses in the modeling and has been shown to perform better than existing approaches when the drug responses are correlated. Detailed analysis of the provided features is included in the Supplementary Material.Availability and ImplementationThe framework has been implemented as a R package IntegratedMRF, which can be downloaded from https://cran.r-project.org/web/packages/IntegratedMRF/index.html, where further explanation of the package is available.Supplementary informationSupplementary data are available at Bioinformatics online.
TI - IntegratedMRF: random forest-based framework for integrating prediction from different data types
JF - Bioinformatics
DO - 10.1093/bioinformatics/btw765
DA - 2017-02-06
UR - https://www.deepdyve.com/lp/oxford-university-press/integratedmrf-random-forest-based-framework-for-integrating-prediction-GlwKAot3Vw
SP - 1407
EP - 1410
VL - 33
IS - 9
DP - DeepDyve
ER -