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A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug–Disease Associations.
- Source :
-
Journal of Computational Biology . Aug2023, Vol. 30 Issue 8, p937-947. 11p. - 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10665277
- Volume :
- 30
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Journal of Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 169928043
- Full Text :
- https://doi.org/10.1089/cmb.2023.0078