Abstract
Anonymous adverse response to medicines available on the flea market presents a major health threat and bounds exact judgment of the cost/benefits trade-off for drugs. Link prediction is an imperative mission for analyzing networks which also has applications in other domains. Compared with predicting the existence of a link to determine its direction is more difficult. Adverse Drug Reaction (ADR), leading to critical burden on the health of the patients and the system of the health care. In this paper, is a study of the network problem, pointing on evolution of the linkage in the network setting that is dynamic and predicting adverse drug reaction. The four types of node: drugs, adverse reactions, indications and protein targets are structured as a knowledge graph. Using this graph different dynamic network embedding methods and algorithms were developed. This technique performs incredibly well at ordering known reasons for unfavorable responses.
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Anitha, M.V., Mathew, L.S. (2020). Dynamic Link Prediction in Biomedical Domain. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_25
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DOI: https://doi.org/10.1007/978-3-030-37218-7_25
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