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• W2343107734 abstract "Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., orphan nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the new drug and new target cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some new drug and new target cases and found that GRMF predicted the left-out interactions reasonably well." @default.
• W2343107734 created "2016-06-24" @default.
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• W2343107734 date "2017-05-01" @default.
• W2343107734 modified "2023-10-16" @default.
• W2343107734 title "Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization" @default.
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• W2343107734 doi "https://doi.org/10.1109/tcbb.2016.2530062" @default.
• W2343107734 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26890921" @default.
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