PDF(Pubmed)
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.
摘要：
简介：探索药物的潜在功效是一种有效的药物开发方法，具有更短的开发时间和更低的成本。最近,已经引入了几种计算药物重定位方法来学习潜在关联预测的多特征。然而,充分利用科学文献中的大量信息来增强药物-疾病关联预测是一个巨大的挑战。方法：我们构建了一种药物-疾病关联预测方法，称为基于文献的多特征融合（LBMFF），有效整合已知药物，疾病,来自公共数据库的副作用和目标关联以及文献语义特征。具体来说,引入预训练和微调BERT模型来提取文献语义信息以进行相似性评估。然后，我们通过具有注意力机制的图卷积网络从构建的融合相似性矩阵中揭示了药物和疾病的嵌入。结果：LBMFF在药物-疾病关联预测中取得了优异的表现，AUC值为0.8818，AUPR值为0.5916。讨论：LBMFF实现了31.67%和16.09%的相对改善，分别,超过第二好的结果，在相同的测试数据集上，与单一特征方法和七种现有的最先进的预测方法进行比较。同时,案例研究已经证实，LBMFF可以发现新的关联，以加速药物开发。建议的基准数据集和源代码可在以下网址获得：https://github.com/kang-hongyu/LBMFF。
