期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 1, 页码 451-462出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz152
关键词
drug-target interaction; machine learning; ensemble learning; cascade deep forest
资金
- National Key Research and Development Program of China [2016YFA0501703]
- National Natural Science Foundation of China [31601074, 61872094, 61832019]
Drug-target interactions are crucial in drug discovery, but existing prediction methods suffer from low precision and high false-positive rates. The proposed DTI-CDF model significantly outperforms traditional methods and accurately predicts new DTIs.
Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases.
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