AI Article Synopsis
- Determining drug-disease associations (DDAs) is crucial for drug development, but current prediction methods lack diversity in feature extraction.
- A new graph representation method using a light gradient boosting machine (GRLGB) was introduced, which incorporates both network topology and biological knowledge to extract features from a heterogeneous network.
- GRLGB showed promising results on two datasets through 10-fold cross-validation and successfully identified novel DDAs in case studies involving anxiety disorders and the drug clozapine.
Article 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.
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| http://dx.doi.org/10.1089/cmb.2023.0078 | DOI Listing |
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