Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.
The Hong Kong Polytechnic University, Hong Kong SAR, China.
BMC Med Inform Decis Mak. 2021 Nov 4;21(Suppl 1):308. doi: 10.1186/s12911-021-01648-x.
Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs.
In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models.
The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.
疾病-药物关联为药物发现和疾病治疗提供了重要信息。许多疾病-药物关联尚未被观察到或未知，而验证这些关联的试验既耗时又昂贵。为了更好地理解和探索这些有价值的关联，开发用于预测未观察到的疾病-药物关联的计算方法将是有用的。随着描述疾病和药物的各种数据集的出现，构建描述疾病和药物之间潜在相关性的模型变得更加可行。
在这项工作中，我们提出了一种新的预测方法，称为 LMFDA，它分几个阶段工作。首先，它研究药物化学结构、疾病 MeSH 描述符、疾病相关表型术语和药物-药物相互作用。在此基础上，构建不同来源的相似性网络，以丰富药物和疾病的表示。基于融合的疾病相似性网络和药物相似性网络，LMFDA 计算了数据库中每对疾病和药物的关联得分。该方法在 Fdataset 和 Cdataset 上均取得了良好的性能，AUROC 分别为 91.6%和 92.1%，优于许多现有的计算模型。
LMFDA 的新颖之处在于引入了使用低秩张量的多模态融合来融合多个相似网络，并结合矩阵补全技术来预测潜在的关联。我们已经证明，LMFDA 能够对准确的疾病-药物关联推断显示出出色的网络集成能力，并在高级方法上取得了实质性的改进。总体而言，在两个真实网络数据集上的实验结果表明，LMFDA 能够提供出色的检测性能。结果还表明，尽可能完善具有更多领域知识的相似网络是药物重定位的一个有前途的方向。
