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Issue 2, 2017
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Exploration and analysis of drug modes of action through feature integration

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Abstract

Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests. Anticipating MoA can help to discover new uses for approved drugs. Here we first used a drug-set enrichment analysis method to discover significant biological activities in every mode of action category. Then, we proposed a new computational model, a probability ensemble approach based on Bayesian network theory, which integrated chemical, therapeutic, genomic and phenotypic properties of over a thousand of FDA approved drugs to assist with the prediction of MoA. 10-fold cross validation tests demonstrate that this method can achieve better performances than four other methods with the area under the receiver operating characteristic (ROC) curves. Finally, we further conducted a large-scale prediction for drug–MoA pairs. Using the Cardiovascular Agents category as an example, several predicted drug–MoA pairs were supported by literature resources.

Graphical abstract: Exploration and analysis of drug modes of action through feature integration

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Publication details

The article was received on 07 Sep 2016, accepted on 21 Dec 2016 and first published on 16 Jan 2017


Article type: Paper
DOI: 10.1039/C6MB00635C
Mol. BioSyst., 2017,13, 425-431

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    Exploration and analysis of drug modes of action through feature integration

    M. Xin, J. Fan, M. Liu and Z. Jiang, Mol. BioSyst., 2017, 13, 425
    DOI: 10.1039/C6MB00635C

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