Abstract
Experimentally identifying previously unknown drug-drug interactions (DDIs) that might cause potentially adverse drug reactions or alter drug’s effectiveness when a combination of two or more drugs are used is a costly task. By contrast, many computational-algorithmic approaches have been used as a faster solution to the problem. Our current research efforts have been directed toward comparative performance evaluation of several approaches for discovering and classifying previously unknown interactions between two drugs. In this research, the task of discovering new DDIs have been formalized as a link prediction task in an interaction network constructed on the basis of previously known drug interactions. Several approaches for link prediction have been experimented with to find empirical evidence of their performance standing. Classifying the interaction type of a newly discovered DDI on the basis of the molecular compound of the drugs involved have also been explored.
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This work was partially financed by the Faculty of Computer Science and Engineering at the “Ss. Cyril and Methodius” University.
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Stefanovska, E., Gievska, S. (2022). Predicting and Classifying Drug Interactions. In: Antovski, L., Armenski, G. (eds) ICT Innovations 2021. Digital Transformation. ICT Innovations 2021. Communications in Computer and Information Science, vol 1521. Springer, Cham. https://doi.org/10.1007/978-3-031-04206-5_3
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