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A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations.
- Source :
-
Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2023 Aug; Vol. 30 (8), pp. 937-947. Date of Electronic Publication: 2023 Jul 24. - Publication Year :
- 2023
-
Abstract
- Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.
- Subjects :
- Reproducibility of Results
Algorithms
Proteins
Computational Biology methods
Subjects
Details
- Language :
- English
- ISSN :
- 1557-8666
- Volume :
- 30
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Journal of computational biology : a journal of computational molecular cell biology
- Publication Type :
- Academic Journal
- Accession number :
- 37486669
- Full Text :
- https://doi.org/10.1089/cmb.2023.0078