There is an equivalent Rucaparib term for target combinations with experimental sensitivity, denotede. We begin with the target combinations with experimen tal sensitivities. For converting the target combinations with experimental sensitivity, we binarize those target combinations, regardless of the number of targets, where the sensitivity is greater thane. The terms that represent a successful treatment are added to the Boolean equation. Furthermore, the terms that have sufficient sensitivity can be verified against the drug representation data to reduce the error. To find the terms of the network Boolean equation, we begin with all possible target combinations of size 1. If the sensitivity of these single targets are suf ficient relative toi ande, the target is binarized.

any further addition of targets will only improve the sensitivity as per Inhibitors,Modulators,Libraries rule 3. Thus, we can consider this target completed with respect to the equation, as we have created the mini mal term in the equation for the target. If the target is not binarized at that level, we expand it by including all pos sible combinations of two Inhibitors,Modulators,Libraries Inhibitors,Modulators,Libraries targets including the target in focus. We continue expanding this method, cutting search threads once the binarization threshold has been reached. The method essentially resembles a breadth or depth first search routine over n branches to a maximum depth of M. This routine has time complexity of O, and will select the minimal terms in the Boolean equation. The D term results from the cost of a single inference.

The time complexity of this method is significantly lower than generation of the complete TIM and optimizing the resulting TIM to a minimal Boolean equation. For the minimal Inhibitors,Modulators,Libraries Boolean equation generation algorithm Inhibitors,Modulators,Libraries shown in algorithm 2, let the function binary return the binary equivalent of x given the number of targets in T, and let sensitivity return the sensitivity of the inhibition combination x for the target set T. With the minimal Boolean equation created using Algorithm 2, the terms can be appropriately grouped to generate an equivalent and more appealing mini mal equation. To convey the minimal Boolean equation to clinicians and researchers unfamiliar with Boolean equations, we utilize a convenient circuit representation, as in Figures 2 and 3. These circuits were generated from two canine subjects with osteosarcoma, as discussed in the results section.

The circuit diagrams are organized by grouped terms, which free overnight delivery we denote as blocks. Blocks in the TIM circuit act as possible treatment combinations. The blocks are orga nized in a linear OR structure. treatment of any one block should result in high sensitivity. As such, inhibition of each target results in its line being broken. When there are no available paths between the beginning and end of the circuit, the treatment is considered effective.