IntegratedMRF: random forest-based framework for integrating prediction from different data types
IntegratedMRF: random forest-based framework for integrating prediction from different data types
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DOI：
10.1093/bioinformatics/btw765
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发表时间：
2017-05-01
期刊：
影响因子：
5.8
通讯作者：
Pal, Ranadip
中科院分区：
文献类型：
--
作者：
Rahman, Raziur;Otridge, John;Pal, Ranadip
IntegratedMRF是一个开源的R实现，用于使用单变量或多变量随机森林集成来自各种基因组表征的药物反应预测，其中包括各种错误估计技术选项。集成框架是在NCI-DREAM药物敏感性预测挑战中基于随机森林的方法的上级性能之后开发的。计算框架可以应用于估计平均值和置信区间的药物反应预测误差的基础上集成方法与各种组合的遗传和表观遗传特征作为输入。该软件包中包含的多变量随机森林实现将建模中输出响应之间的相关性结合起来，并且已被证明在药物响应相关时比现有方法表现更好。对所提供功能的详细分析见补充材料。
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.