For MCF7 cell line, as the result of selleckchem both gene signature selection and quality control, 1564 samples from 747 drugs are identified and removed and 1347 samples from 504 drugs are passed to BRAC MoNet construction. These samples can be considered to correctly capture the treatment effect on the MCF7 cell line and were therefore used for subsequent investigation. Mode Inhibitors,Modulators,Libraries of Action BRCA MoNet generation A compound mode of Action is defined as a group Inhibitors,Modulators,Libraries of compounds that share similar gene signature expression patterns. Drugs forming one MoA will therefore have sub stantially shared genes in their signature gene set, which also have similar expression patterns. To obtain MoAs, clustering is applied to group the drugs with similar signa ture gene expression patterns.

Multiple clustering algo rithms exist and the simple yet effective Hierarchical Clustering method is adopted in our work. There are two major reasons to choose HC. First, the number of clus ters is not Inhibitors,Modulators,Libraries required for HC. second, it is reasonable to expect that some drugs form distinct MoAs by itself and HC can produce clusters with a single member. To per form HC, a Inhibitors,Modulators,Libraries distance matrix that measures pair wise dis tances between drugs was obtained after quality control. With this distance matrix, a total of 109 MoAs were obtained at a threshold and a BRCA MoNet was constructed. In this network, each node represented one drug. a group of nodes share the same color edges represent a BRCA MoA obtained by HC. For each MoA, the betweenness centrality of each drug was calculated and the drug with the largest betweenness centrality was set to be the center of the MoA.

Two MoAs were linked with a black edge if the distance between them was smaller than the threshold and this link indicated the secondary Inhibitors,Modulators,Libraries level relationship between two MoAs. BRCA MoNet application After the BRCA MoNet being constructed, its prediction power was tested. Three questions were investigated. First, can BRCA MoNet predict correct drug MoA Second, to what extent can BRCA MoNet predict the drug MoA of an unknown or new drug Third, to what extend can BRCA MoNet recommend drugs for patients To answer these questions, independent microarray expression data sets were downloaded from Gene Expression Omnibus for the investigation. In order to avoid possible platform and experimental bias, the following criteria were followed when we select the data sets.

First, the data must be compound treated on the MCF7 cell line and contain one or multiple control samples. this was consistent with the condition of the cMap data. Second, we only choose those datasets that were treated with drugs existed in the cMap project or of known treatment effect in breast can cer. Third, to avoid possible across platform complication, selleckchem Volasertib the data must be generated from the same platform as the cMap data, or GPL96. With the above considerations, the follow ing three case studies were carried out.