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

Raziur Rahman, John Otridge, Ranadip Pal

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1407-1410
Number of pages4
JournalBioinformatics
Volume33
Issue number9
DOIs
StatePublished - May 1 2017

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