This is a major resource for collecting effective drug combinations from the literature. The tar get protein information, the Anatomical Therapeutic Chemical code annotation of the drugs and pro tein subcellular localizations, were extracted from Drug Bank. Drug combinations Bicalutamide order that do not have ATC codes for the corresponding drug components and com binations with none or unclear efficacy were discarded. Finally, 194 effective drug combinations were obtained, including 76 approved combinations, 64 clinical combi nations and 54 preclinical combinations. We then split where n ranges from 1 to 5. In this study, n 3 is adopted considering that only a few drugs have the same ATC codes at the 5th level. Drug combination prediction We assume that two drugs are more likely to be com bined if they share a large number of common drugs in the drug cocktail network.

For example, if two drugs d1 and d2 with respective n1 and n2 partners have m in common in the drug cocktail network, there will be three groups in the neighborhood of the two drugs, i. e. m drugs that are the neighbors of both drug d1 and d2. n1 m partners that are the neighbors of drug d1 only. and n2 m partners are the neighbors of drug d2 only. Suppose that there are totally N drugs in the drug combination network, then a p value between d1 and d2 can be calculated using the following equa tion the combinations with more than two drug components into combination pairs, resulting in 239 drug combina tion pairs. These drug combinations were used to con struct a drug cocktail network, where the nodes represent drugs and the edges represent combina tions, respectively.

In the drug cocktail network, the size If two drugs share more common drugs compared with all of their neighbors, the p value computed by equation will be closer to 0, which means they are more likely to be combined. We use the equation to compute the p values for all possible combinations and then rank the values in ascending order. As drug pairs with lower p values are more likely to be combined, the prediction of effective drug combinations can be made given a certain p value threshold. We term this framework that explores the drug cocktail network and predicts possible Anacetrapib drug com bination as DCPred and assess its performance for inferring effective drug combi nations based on the curated drug combinations dataset.

Background The use of animal models is essential in the study of many human disorders, especially in the occasions when human patients are inaccessible, or ethical issue pre vents using human subjects in such studies. Animal models can greatly reduce the costs selleck inhibitor of research and thus they are available and affordable to a broad scienti fic community. Animal models have been proved to be important in the areas of chronic wasting diseases, i. e. Alzheimer, cancers, and new drug develop ment.