Within each bin, we want to mini mize the variation between the predicted sensitivity for the target combination, P, and the experimental sensitivities, Y. This notion is equivalent to mini mizing the inconsistencies AGI-6780? of the experimental sensitivity values with respect to the predicted sensitivity values for all known target combinations for any set of targets, which in turn suggests the selected target set effectively explains the mechanisms by which the effective drugs are able to kill cancerous cells. Numerically, we can calculate the inter bin sensitivity error using the following equation, This analysis has one notable flaw, if we attempt to min T bins j��bin |P ? Y | only separate the various drugs into bins based on inter bin sensitivity error, we can create an over fitted solution by breaking each drug into an individual bin.

We take two steps to avoid this. First, we attempt to minimize the number of targets during construction of T0. Second, we incorporate an inconsistency term to account for target behavior that we consider to be biologically inaccurate. To expand on the above point, we consider there are two complementary rules by which kinase targets behave. Research has shown that the bulk of viable kinase tar gets behave as tumor promoters, proteins whose presence and lack of inhibition is related to the continued survival and growth of a cancerous tumor. These targets essentially have a positive correlation with cancer progression.

This For brevity, we will denote the scoring function of a target set with respect to the binarized EC50 values S and the scaled sensitivity scores Y, As the S and Y sets will be fixed when target set generation begins, we reduce this notation further to. Note that T ? K where K denotes the set Anacetrapib of all possible targets. 2|K| is the total number of possibilities for T which is extremely huge and thus prohibits exhaustive search. Thus the inherently nonlinear and computational inten sive target set selection optimization will be approached through suboptimal search methodologies. A number of methods can be applied in this scenario and we have employed Sequential Floating Forward Search to build the target sets. We selected SFFS as it generally has fast convergence rates while simultaneously allowing for a large search space within a short runtime.

Addition ally, it naturally incorporates the desired target set mini mization aim as SFFS will not add features that provide no benefit. We present the SFFS algorithm for construction of the minimizing target set in algorithm 1. Rule 3 follows from the first two rules, rule 1 provides that any superset will have greater sensitivity, and rule 2 provides that any subset will have lower sensitivity. To apply rule 3 in practical situations, we must guaran tee that every combination will have a subset and superset with ref 1 an experimental value.