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. 2023 Nov 22;24(1):442.
doi: 10.1186/s12859-023-05572-x.

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

Ramin Amiri  1 , Jafar Razmara  2 , Sepideh Parvizpour  3   4 , Habib Izadkhah  1
Affiliations

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

Ramin Amiri et al. BMC Bioinformatics. .

Abstract

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.

Keywords: Data integration; Deep learning; Drug repurposing; Machine learning.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Overview of the proposed approach
Fig. 2
Fig. 2
Summary of the CNN architecture
Fig. 3
Fig. 3
IDDI-DNN training progress in terms of accuracy and loss on training and testing sets
Fig. 4
Fig. 4
ROC and PR curves obtained by IDDI-DNN and other state-of-the-art methods on (A) the gold standard dataset, and (B) DNdataset
Fig. 5
Fig. 5
The average PR and ROC obtained by IDDI-DNN after 150 iterations
Fig. 6
Fig. 6
Confusion matrix
Fig. 7
Fig. 7
Prediction of new drug indications by different methods with the maximum precision. The results except for IDDI-DNN are from [41]
See this image and copyright information in PMC

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