Identifies statistically significant targets in designing multi-drug therapy regimes. SMuRFS is a method based on a sequential algorithm dedicated to variable selection procedure. It is capable to create sparse set of features without resorting to regularization techniques or any other modeling assumptions. The application was tested on simulated data as well as on Genomics of Drug Sensitivity for Cancer dataset.
Assists users in quantifying compound quality. QED provides a quantitative metric for assessing drug-likeness. The provided values can range between zero (all properties unfavorable) and one (all properties favorable). The functions are based on the underlying distribution data of drug properties. It allows users to submit chemical compounds and subsequent prediction of drug sensitivity in terms of logIC50 (µM).
Combines kernel-based non-linear dimensionality reduction and binary classification (or regression). kbmtl main feature is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps users to eliminate off-target effects and drug-specific experimental noise. This method allows users to handle missing phenotype values owing to experimental conditions and quality control reasons. It obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.
Identifies targeted biological pathways for drugs with unclear mechanism of action. iFad is an R package, based on a Bayesian sparse factor analysis model, that merges information from paired gene expression and drug sensitivity from the same set of samples. The application is designed for a natural incorporation of prior knowledge about the connectivity structure of biological pathways.
Allows prediction of drug response from patient tumor gene expression data. pRRophetic can be used to predict non-clinical phenotypes, user-defined training sets and continuous and categorical phenotypes. It employs the Cancer Genome Project (CGP) cell lines as a training set. This tool’s accuracy prediction will ultimately vary from drug to drug based on the predictive power of expression under that particular set of conditions and the appropriateness of cell lines as a model of in vivo drug response.
A webserver developed for predicting priority and potency of an anticancer drug against a cancer cell line using its genomic features, in order to throw light in the field of personalized medicine, particularly in designing patient-specific anticancer drugs. We investigated drug profile of 24 anticancer drugs tested against a large number of cell lines in order to understand the relation between drug resistance and altered genomic features of a cancer cell line. We detected frequent mutations, high expression and high copy number variations of certain genes in both drug resistant cell lines and sensitive cell lines.
Allows to calculate and mine drug-response data. GR Calculator permits to proceed analysis, and visualization of growth rate (GR) inhibition metrics. It can quantify how drug sensitivity changes in the face of variables that affect division rate. The tool provides an intuitive user interface that facilitates quick adoption of GR metrics. The website offers to users two examples to show capabilities of the tool.