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Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning.
- Source :
-
Computational & Mathematical Methods in Medicine . 10/10/2022, p1-10. 10p. - Publication Year :
- 2022
-
Abstract
- Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method can effectively find the correlation between drugs and diseases, and the prediction accuracy is 82.7%. We found that there are 8 pairs of drug-disease relationships that have not yet been reported, and 5 of them have pharmacodynamic effects on Parkinson's disease. We also found that a key linkage between Parkinson's disease and phenylhexol, a drug for the treatment of Parkinson's disease α-synuclein and tau protein, provides a useful exploration for the effectiveness of the treatment of Parkinson's disease. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1748670X
- Database :
- Academic Search Index
- Journal :
- Computational & Mathematical Methods in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 159582416
- Full Text :
- https://doi.org/10.1155/2022/7035634