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One of the potential improvements to our approach is the inclusion of more data in our meta-analysis. One possible approach is to adjust the Bayesian model; new hyperpriors can be designed for the distribution of treatment effectiveness. Another way to make more information available to the clustering algorithm would be to provide more information on absolute efficacy, in the form of negative edges. If a certain drug was found in a study to be completely ineffective against a particular cancer, a separate ineffective hyperedge should be created to include this information in clustering considerations. Although hemonc.org did not have negative information as it was meant to be a treatment guideline database, a more comprehensive database would yield click here better clusters. In addition to efficacy data, the inclusion of direct biological mechanisms DNA Synthesis inhibitor in our inductive approach could provide a powerful syncretic method that may solve the problem of discovering new subtypes of cancer as well. Furthermore, the possibility remains that the unit of our clustering, the cancer treatment context, is not the best disease unit to use. For example, gastrointestinal stromal tumors are included in the sarcoma contexts, but are treated differently from most sarcomas. Dividing cancers into contexts in some other way may provide more information or better clusters. In addition to addressing these problems in clinical oncology, cancer treatment network analysis can also yield better ways to organize other processes relating to cancer care, such as drug production. Cancer drug shortages are a major problem in cancer treatment [27]. We can track sudden bursts in publications in particular clusters using a network analysis algorithm running on a cancer treatment network. Thus, it may be possible to predict drug shortages in the future, ensuring that production can be increased before the demand spike. Analytical models of cancers based on biological etiologies have characterized cancer meta-analysis and drug inference thus far. The inductive, global, treatment-based approach outlined in this paper directly analyzes treatment efficacy data to provide a unified solution to both cancer reclassification and drug inference. Our combination of topological abstraction UNC2881 and Bayesian techniques was effective at elucidating the structure of the cancer treatment network provided by the regimen database, discovering hidden commonalities between cancers whose validity is confirmed by our Fisher��s exact test p-values (p