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.File | Dimensione | Formato | |
---|---|---|---|
CIBCB_2019-6.pdf
Accesso riservato
Dimensione
502.35 kB
Formato
Adobe PDF
|
502.35 kB | Adobe PDF | Visualizza/Apri |
CIBCB_2019_preprint.pdf
accesso aperto
Dimensione
502.35 kB
Formato
Adobe PDF
|
502.35 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.