Drug–disease association prediction with literature based multi-feature fusion

Abstract

Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge.Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism.Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916.Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

Drug repositioning based on weighted local information augmented graph neural network

“…To showcase the superiority of the DRAGNN model, we conducted a comparison with five advanced models on three datasets: Fdataset, Cdataset and LRSSL. The models included in the comparison were DRWBNCF [ 23 ], LBMFF [ 34 ], SCMFDD [ 35 ], SCPMF [ 8 ] and HNRD [ 36 ].…”

Drug Repurposing and Lysosomal Storage Disorders: A Trick to Treat

“…Coupling high-throughput data production with data infrastructure and computational powers makes it feasible to use machine learning (ML) to aid drug repositioning. ML methods could be implemented during the coupling of the known drug with the disease-causing protein by aiding the recognition of potential drug–disease associations (e.g., through information networks [ 86 , 87 ] or embeddings [ 88 ]). The same methods could produce valid drug combinations that potentiate the effect of the cure [ 89 ].…”

Research on Efficient and Low-cost Drug-disease Association Prediction Method Based on Dual Attention in Heterogeneous Networks

“…We compare this experiment with other drug-disease association algorithms to demonstrate the effectiveness of our experiment. We conduct a comparative experiment between the method of this paper and the methods of Kang [13] and Chen [14] on the same dataset. As shown in Table 1, the experimental method used in this paper possesses an accuracy rate of 98.5%, which is far superior to other methods, indicating that the method in this paper can more accurately make a better prediction of whether a drug can treat a disease.…”