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Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method

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Abstract

Prediction of new associations between drugs and targeting pathways can provide valuable clues for drug discovery & development. However, information integration and a class-imbalance problem are important challenges for available prediction methods. This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method. Firstly, we built a drug–disease–pathway network and combined the drug–disease and pathway–disease features obtained by different types of feature profiles. Then we applied a latent semantic analysis (LSA) method to perform dimension reduction by combining positive-unlabeled (PU) learning and k nearest neighbors (KNN) method. The experimental results showed that our method can achieve a higher AUC (the area under the ROC curve) and AUPR (the area under the PR curve) than other typical methods. Furthermore, some interesting drug–pathway interaction pairs were identified and validated.

Graphical abstract: Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method

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Publication details

The article was received on 20 Jul 2017, accepted on 27 Sep 2017 and first published on 12 Oct 2017


Article type: Paper
DOI: 10.1039/C7MB00441A
Citation: Mol. BioSyst., 2017, Advance Article
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    Prediction of drug–pathway interaction pairs with a disease-combined LSA-PU-KNN method

    F. Chen, H. Jiang and Z. Jiang, Mol. BioSyst., 2017, Advance Article , DOI: 10.1039/C7MB00441A

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