https://www.selleckchem.com/pr....oducts/thiomyristoyl
The experimental results demonstrate the effectiveness of our proposed method for DDI prediction and significant improvement compared to other state-of-the-art benchmark methods. Moreover, we apply a specialized random forest classifier in the positive-unlabeled (PU) learning setting to enhance the prediction accuracy. Experimental results reveal that the model improved by PU learning outperforms the original method DDI-MDAE by 7.1% and 6.2% improvement in AUPR metric respectively on 3-fold cross-validation (3-CV) and 5-fold cross
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