A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks.

Autor: Amiri R; Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran., Razmara J; Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran. razmara@tabrizu.ac.ir., Parvizpour S; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.; Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran., Izadkhah H; Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2023 Nov 22; Vol. 24 (1), pp. 442. Date of Electronic Publication: 2023 Nov 22.
DOI: 10.1186/s12859-023-05572-x
Abstrakt: Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
(© 2023. The Author(s).)
Databáze: MEDLINE
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