But more data does not directly give us new answers. As we see more and more data available to researchers, we need new ways to look at and approach this data to look for meaningful connections. Under this mindset we see companies, organizations, and governments releasing some of their data to crowdsource new approaches to using their data. AstraZeneca, for example, established a partnership with the European Bioinformatics Institute, the Sanger Institute, Sage Bionetworks and the DREAM community and released approximately 11.5k experimentally tested drug combinations measuring cell viability in several cancer cell lines and challenged the public domain to explore potential synergistic drug combinations . The question remains, however, how this data can be used to get new insights in the applicability of drug combinations. Combinations of drugs are used in medicine to treat ailments that are complex diseases, such as cancer, and can have synergistic effects . Drug synergy is defined as the effect of a combination of drugs that is greater than summing the effects of the individual drugs together. There are different models for predicting effects based on component-based approaches, with the independent model (IA) and the concentration addition model (CA) being described by Tanaka and Tada . The CA model is for similarly acting chemicals, while the IA model is used to describe chemicals with different modes of action. The CA model is generally considered to be a better predictor of the synergy than the IA model in the case of a mixture of many substances . The Loewe-Additivity model is an example of a CA model and follows the model of similarly acting substances if they target the same targets and processes as defined by the equation Synergy Loewe = d1 D1 + d2 D2 = 1 for additivity, where if Synergy Loewe < 1 is synergistic and Synergy Loewe > 1 is antagonistic with d1 and d2 represent doses that have an effect when in combination and D1 and D2 have an effect when used alone.