Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
BMC Bioinformatics. 2022 Dec 7;23(1):526. doi: 10.1186/s12859-022-05083-1.
Drug-drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous research demonstrates the effectiveness of using matrix completion approaches based on known drug interactions to predict unknown Drug-drug interactions. However, in the case of a new drug, where there is limited or no knowledge regarding the drug's existing interactions, such an approach is unsuitable, and other drug's preferences can be used to accurately predict new Drug-drug interactions.
We propose adjacency biomedical text embedding (ABTE) to address this limitation by using a hybrid approach which combines known drugs' interactions and the drug's biomedical text embeddings to predict the DDIs of both new and well known drugs.
Our evaluation demonstrates the superiority of this approach compared to recently published DDI prediction models and matrix factorization-based approaches. Furthermore, we compared the use of different text embedding methods in ABTE, and found that the concept embedding approach, which involves biomedical information in the embedding process, provides the highest performance for this task. Additionally, we demonstrate the effectiveness of leveraging biomedical text embedding for additional drugs' biomedical prediction task by presenting text embedding's contribution to a multi-modal pregnancy drug safety classification.
Text and concept embeddings created by analyzing a domain-specific large-scale biomedical corpora can be used for predicting drug-related properties such as Drug-drug interactions and drug safety prediction. Prediction models based on the embeddings resulted in comparable results to hand-crafted features, however text embeddings do not require manual categorization or data collection and rely solely on the published literature.
药物-药物相互作用（DDI）是可预防的医疗伤害的原因，通常导致医生和急诊室就诊。先前的研究表明，基于已知药物相互作用使用矩阵补全方法来预测未知的药物-药物相互作用是有效的。然而，对于一种新的药物，由于对该药物现有的相互作用的了解有限或没有，这种方法是不合适的，可以使用其他药物的偏好来准确预测新的药物-药物相互作用。
我们提出了邻接生物医学文本嵌入（ABTE），通过使用一种混合方法来解决这个限制，该方法结合了已知药物的相互作用和药物的生物医学文本嵌入，以预测新的和已知药物的药物-药物相互作用。
我们的评估表明，与最近发表的 DDI 预测模型和基于矩阵分解的方法相比，该方法具有优越性。此外，我们比较了 ABTE 中不同文本嵌入方法的使用，发现涉及嵌入过程中生物医学信息的概念嵌入方法对这项任务的性能最高。此外，我们通过展示文本嵌入对多模态妊娠药物安全性分类的贡献，证明了利用生物医学文本嵌入进行其他药物的生物医学预测任务的有效性。
通过分析特定领域的大规模生物医学语料库创建的文本和概念嵌入可以用于预测药物相关属性，如药物-药物相互作用和药物安全性预测。基于嵌入的预测模型与手工制作的特征相比产生了可比的结果，但是文本嵌入不需要手动分类或数据收集，只依赖于已发表的文献。
