The training panel and the testing panel have no drugs in common. Each of the 60 train ing drugs is applied to the network, and the sensitivity for each drug is recorded. The generated TIM is then sam pled using the test Vandetanib cost panel which determines the predicted sensitivities of the test panel. The synthetic experiments were performed for 40 randomly generated cancer sub networks for each of n 6, 10 active targets in the network. The active targets are Inhibitors,Modulators,Libraries those which, when inhib ited, may have some effect on the cancer downstream. To more accurately mimic the Boolean nature of the biolog ical networks, a drug which does not satisfy any of the Boolean network equations will Inhibitors,Modulators,Libraries have sensitivity 0, a drug which satisfies at least one network equation will have sen sitivity 1.

The inhibition profile of the test drugs is used to predict the sensitivity of the new drug. The average number of correctly predicted drugs for each n is reported in Table 7. This synthetic modeling approach generally produces respectable levels of accuracy, with accuracies Inhibitors,Modulators,Libraries ranging from 89% to 99%. 60 drugs for training mimics the drug screen setup used Inhibitors,Modulators,Libraries by our collaborators and testing 20 drugs for predicted sensitivity approximates a sec ondary drug screen to pinpoint optimal therapies. The performance of the synthetic data shows fairly high relia bility of the predictions made by the TIM approach. We have also tested our algorithm on another set of ran domly generated synthetic pathways. The detailed results of the experiment are included in Additional file 1.

A large number of testing samples were used for each pathway prediction and the results indicate an average error of less than 10% for multiple scenarios. In comparison, the aver age error with random predictions was 44%. The average correlation coefficient of the prediction to actual sensi tivity for the 8 sets of experiments was 0. 91. The average Inhibitors,Modulators,Libraries correlation coefficient with random predictions was 0. We also report the standard deviation of the errors and for a representa tive example, the 10 percentile of the error was 0. 154 and 90 percentile 0. 051, thus the 80% prediction interval for prediction u was. The results of the synthetic experiments on different randomly generated pathways shows that the approach presented in the paper is able to utilize a small set of training drugs from all possible drugs to generate a high accuracy predictive model.

Methods In this section, we provide an overview of the model design and inference from drug perturbation data for personalized therapy. Mathematical inhibitor Bosutinib formulation Let us consider that we have drug IC50 data for a new pri mary tumor after application of m drugs in a controlled drug screen. Let the known multi target inhibiting sets for these drugs be denoted by S1, S2.Sm obtained from drug inhibition studies.