IntegratedMRF: random forest-based framework for integrating prediction from different data types.
IntegratedMRF 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 .



The framework has been implemented as a 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.



ranadip.pal@ttu.edu.



Supplementary data are available at Bioinformatics online.
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Raziur
R
Rahman
Leidos Biomedical Research Inc, Frederick, MD 21701, USA.
John
J
Otridge
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Ranadip
R
Pal
D000970:Antineoplastic Agents / Q000627:therapeutic use; D054316:Biomarkers, Pharmacological; D019175:DNA Methylation; D005786:Gene Expression Regulation; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D057285:Precision Medicine / Q000379:methods; D012984:Software; D013223:Statistics as Topic / Q000379:methods*; D059467:Transcriptome
D000970:Antineoplastic Agents; D054316:Biomarkers, Pharmacological