Drug discovery is a high cost and high risk process, thus finding new uses for approved drugs, i.e. drug repositioning, via computational methods has become increasingly interesting. In this study, we present a new network-based approach for predicting potential new indications for existing drugs through their connections with other biological entities. For this aim, we first built a large network integrating drugs, proteins, biological pathways and drugs' categories as nodes of the network, and connections between such nodes as links of the network. Our method leverages the Non-Negative Matrix Tri-Factorization reconstruction of adjacency matrices in order to predict novel category-drug links, i.e. a new category (or use)associated with a drug, taking the entire network information into account. We tested our method on a set of 1,120 drugs labeled with ten categories; when we hide to the method the 10% of the drug-category associations, it was able to infer those missing values with a recall of 60% and a precision of 70%. Precision and recall remain higher than a Random Classifier in case of larger percentage of hidden links, demonstrating the robustness of the method. Also, we were able to predict novel drug-label associations not yet reported in the repository. Finally, we favorably compared our method with a state of the art method for drug repositioning; the NMTF method achieved an average precision score of 0.68 vs. the 0.55 score of the state of the art method.

Non-negative Matrix Tri-Factorization for Data Integration and Network-based Drug Repositioning

Ceddia, Gaia;Pinoli, Pietro;Ceri, Stefano;Masseroli, Marco
2019-01-01

Abstract

Drug discovery is a high cost and high risk process, thus finding new uses for approved drugs, i.e. drug repositioning, via computational methods has become increasingly interesting. In this study, we present a new network-based approach for predicting potential new indications for existing drugs through their connections with other biological entities. For this aim, we first built a large network integrating drugs, proteins, biological pathways and drugs' categories as nodes of the network, and connections between such nodes as links of the network. Our method leverages the Non-Negative Matrix Tri-Factorization reconstruction of adjacency matrices in order to predict novel category-drug links, i.e. a new category (or use)associated with a drug, taking the entire network information into account. We tested our method on a set of 1,120 drugs labeled with ten categories; when we hide to the method the 10% of the drug-category associations, it was able to infer those missing values with a recall of 60% and a precision of 70%. Precision and recall remain higher than a Random Classifier in case of larger percentage of hidden links, demonstrating the robustness of the method. Also, we were able to predict novel drug-label associations not yet reported in the repository. Finally, we favorably compared our method with a state of the art method for drug repositioning; the NMTF method achieved an average precision score of 0.68 vs. the 0.55 score of the state of the art method.
2019
Proceedings of the International Conference on Computational Intelligence in Bioinformatics and Computational Biology – CIBCB 2019
978-1-7281-1462-0
data integration , drugs , matrix decomposition , medical computing , pattern classification , proteins, drug repositioning , protein-protein networks , pathways , interaction networks , data integration , link prediction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1102952
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