Further, using this expanded set for validation experiments identified false negatives from the original screen. These results reaffirm the utility of filtering data by pathway membership to identify true positives and also using pathway membership as a search space for false negatives. In a pioneering study, Jones et al. demonstrated the significance of using pathway context in a patient setting [26]. They performed a global analysis of mutations in pancreatic cancers, but Depsipeptide research buy found little overlap in the specific mutations across patients. However, they instead found a core set of signaling pathways that consistently enriched

for patient-specific mutations. They postulate that targeting the physiological consequences of these pathways

instead of the individual mutations would Ixazomib improve therapeutic development [26]. If we consider the discrepancy between RNAi reagent performance across replicates as similar to the mutational differences between patients, these findings present more motivation for using a pathway-centered approach for functional genomic studies. Given the importance of understanding the functional context of a genetic alteration, network methods are a useful computational tool. Additionally, these tools enable the incorporation of multiple data sets and experiments to create more holistic interpretations of biological systems. Because of the availability of many experimental datasets through various

databases, data integration will be influential in future investigations [27]. Here, we review a few integrated Oxalosuccinic acid network approaches and highlight how networks have improved the interpretation of biological investigations and affected further hypothesis generation. In metastatic breast cancer, integrating copy-number variation (CNV) and gene expression data across multiple samples accurately predicted novel drivers of disease [28]. The authors used a refined method for first identifying recurrent CNVs from gene expression data and then used a Bayesian methodology to create a network of mutated genes. From this network, they found master regulators by selecting genes that had a high authority score. Mathematically, the authority score identified genes with a statistically significant number of outgoing connections as compared to the mean number of connections. To test their hypotheses about mediators for breast cancer, they performed an siRNA screen testing the effect of gene interference on cell viability. Of the gene targets that had the greatest effect on cell viability, they found a significant enrichment of their high-authority regulators [28]. This finding demonstrates that networks can synchronize disparate datasets and that network properties are viable characterizations for finding novel regulators. Utilizing a data-integration approach, Huang et al.