Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/ijms22126598
Keywords
drug-target interactions; ensemble model of Multiple Feature Pairs (Ensemble-MFP); model weight sum; support vector machines
Funding
- National Natural Science Foundation of China [61472282, 61672035]
- Educational Commission of Anhui Province [KJ2019ZD05]
- Anhui Province Funds for Excellent Youth Scholars in Colleges [gxyqZD2016068]
- Anhui Scientific Research Foundation for Returned Scholars
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The Ensemble-MFP method shows good prediction performance in new drug prediction, with AUC exceeding 94.0%. By weighting existing feature pairs, this method can effectively make general predictions.
Backgroud: The prediction of drug-target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection. Methods: The prediction method based on the Ensemble model of Multiple Feature Pairs (Ensemble-MFP) is introduced. Firstly, three negative sets are generated according to the Euclidean distance of three feature pairs. Then, the negative samples of the validation set/test set are randomly selected from the union set of the three negative sets in the validation set/test set. At the same time, the ensemble model with weight is optimized and applied to the test set. Results: The area under the receiver operating characteristic curve (area under ROC, AUC) in three out of four sub-datasets in gold standard datasets was more than 94.0% in the prediction of new drugs. The effectiveness of the proposed method is also shown with the comparison of state-of-the-art methods and demonstration of predicted drug-target pairs. Conclusion: The Ensemble-MFP can weigh the existing feature pairs and has a good prediction effect for general prediction on new drugs.
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