Raziur Rahman

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Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows(More)
Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic(More)
Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem(More)
Summary 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(More)
Ultrasound elastography is a medical imaging technique to visualize the tissue stiffness using a conventional ultrasound machine. To compute the elastogram, the radiofrequency echo signals are acquired before and after a small applied deformation. Then the local tissue displacement field is estimated by searching the peak of the cross-correlation function(More)
Precision medicine for cancer involves design of drug sensitivity prediction models that can predict patient response to various drugs. The drug response is usually represented by a single feature such as Area Under the Curve or IC50 derived from the experimental dose response curve. In this article, we consider the idea that predicting the dose response(More)
Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the(More)
A requirement for precision medicine is the accurate prediction of the sensitivity of a given drug on an individual patient. A very common method for this prediction is the use of Random Forest built on Genomic Features such as gene expression. However, effective drug sensitivity prediction requires the use of multiple heterogeneous dataset but it is rare(More)
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