Dynamic Link Prediction in Biomedical Domain
| Autor: | Linda Sara Mathew, M. V. Anitha |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Dynamic network analysis
business.industry Computer science Node (networking) Linkage (mechanical) medicine.disease 01 natural sciences 010305 fluids & plasmas law.invention Non-negative matrix factorization Domain (software engineering) Risk analysis (engineering) law 0103 physical sciences Health care medicine Graph (abstract data type) 010306 general physics business Adverse drug reaction |
| Zdroj: | Computational Vision and Bio-Inspired Computing ISBN: 9783030372170 |
| DOI: | 10.1007/978-3-030-37218-7_25 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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