However, even if we focused on E2 for MCF7 cell line, its ranking is still low. Close look at the detailed http://www.selleckchem.com/products/VX-770.html results revealed that, the ranking E2 treated Inhibitors,Modulators,Libraries MCF7 cell line was a summary of the results from 19 individual E2 treated MCF7 cell line and their enrichment scores did not agree with each other, Among the 19 sam ples, only a few have high enrichment scores. It is very likely that the rest of samples do not have high quality and thus fail to catch the real E2 treatment effect. Another potential Inhibitors,Modulators,Libraries cause for this poor result is the ineffective choice of the Inhibitors,Modulators,Libraries signature genes. However, as a user, we do not have a better way to choose an effective gene set to achieve better prediction. These results underscore the need for quality control and systematic selections of signature genes.

To address the above Inhibitors,Modulators,Libraries challenges, we proposed BRCA MoNet in this paper. BRCA MoNet is advantageous in three aspects compared with cMap. First, it focuses only on breast cancer cell line. Although doing so ignores other cell line data in the cMap data, it nevertheless removes the cell line dependent interference from the true drug effect. Second, a quality control procedure as well as new drug signature gene set selection algorithm are developed to remove the possible noise in cMap data and characterize drugs treatment effect in a more systematic manner. Third, we define a Mode of Action as a group of compounds that share the similar differential gene expres sion signature. Since the drug expression signature is indi Inhibitors,Modulators,Libraries cative of the degree of its sensitivity to a cell, a MoA drug group should possess similar therapeutic effect.

The con struction of MoA introduces extra prediction power. This is because drugs selleckchem with similar treatment effect might be ranked low due to high noise in data if we treat prediction of each drug independently. In contrast, this high noise sample could be ranked high because the query agrees with its MoA. The MoA is also different from other exist ing defined compound groups such as those by their ana tomical therapeutic compound classification since MoA is defined by differential gene expression after treat ment, even though some overlapping between various compound classifications might be expected. The relation ship of different MoAs in terms of their therapeutic effect can be modeled and visualized by a BRCA MoNet. BRCA MoNet presents a global view of drug effects at a genomic level. This network augments and improves the current understanding of compound MoA defined mainly from a physiology perspective, and underscores the relationship of different compounds.