Drug Repositioning Based on Deep Sparse Autoencoder and Drug –Disease Similarity

AbstractDrug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug –disease heterogeneous networks to extract drug–disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The me thod utilizes a deep sparse autoencoder and integrates drug–disease similarities. First, the researchers constructed a drug–disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug–disease associations. Then, we learned t he low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug–disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions wer e subsequently validated by other literature sources.Graphical AbstractSchematic diagrams of data processing and DRDSA model.A Construction of drug and disease feature vectors,B...
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research