Abstract
A major alternative strategy for the pharmacology industry is to find new uses for approved drugs. A number of studies have shown that target binding of a drug often affects not only the intended disease-related genes, leading to unexpected outcomes. Thus, if the perturbed genes are related to other diseases this permits the repositioning of an existing drug. Our aim is to find hidden relations between drug targets and disease-related genes so as to find new hypotheses of new drug-disease pairs. Association Rule Mining (ARM) is a well-known data mining technique which is widely used for the discovery of interesting relations in large data sets. In this study we apply a new computational intelligence approach to 288 drugs and 267 diseases, forming 5018 known drug-disease pairs. Our method, which we call Grammatical Evolution ARM (GEARM), applies the GE optimization technique on the set of rules learned using ARM and which represent hidden relationships among gene targets. The results produced by this combination show a high accuracy of up to 95 % for the extracted rules. Likewise, the suggested approach was able to discover interesting pairs of drugs and diseases with an accuracy of 92 %. Some of these pairs have previously been reported in the literature while others can serve as new hypotheses to be explored.
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Boutorh, A., Pratanwanich, N., Guessoum, A., Liò, P. (2015). Drug Repurposing by Optimizing Mining of Genes Target Association. In: DI Serio, C., Liò, P., Nonis, A., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2014. Lecture Notes in Computer Science(), vol 8623. Springer, Cham. https://doi.org/10.1007/978-3-319-24462-4_18
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DOI: https://doi.org/10.1007/978-3-319-24462-4_18
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