The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for\nselecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if\ncomputational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts havemet with\nlimited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to\nidentify the potential biological effect of such treatment.While these efforts have met with some level of success, there exists much\nopportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response,\nthere is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies\nhave yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular\ndatasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for\ndisease factors.Here, we propose an algorithmto specifically predict synergistic effects of drug combinations on various diseases, by\nintegrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles.We have\ndemonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease\nprediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling\ndata such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to\nbe readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the\nalgorithm can provide the basis for further refinement towards addressing a large clinical need.
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