A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions

Abstract

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug–drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs. To address the above issues, we first leverage Balance theory and Status theory in social networks to reveal the topological patterns among directed pharmacological DDIs, which are modeled as a signed and directed network. Then, we design a novel graph representation learning model named SGRL-DDI (social theory-enhanced graph representation learning for DDI) to realize the multitask prediction of DDIs. SGRL-DDI model can capture the task-joint information by integrating relation graph convolutional networks with Balance and Status patterns. Moreover, we utilize task-specific deep neural networks to perform two tasks, including the prediction of enhancive/depressive DDIs and the prediction of directed DDIs. Based on DDI entries collected from DrugBank, the superiority of our model is demonstrated by the comparison with other state-of-the-art methods. Furthermore, the ablation study verifies that Balance and Status patterns help characterize directed pharmacological DDIs, and that the joint of two tasks provides better DDI representations than individual tasks. Last, we demonstrate the practical effectiveness of our model by a version-dependent test, where 88.47 and 81.38% DDI out of newly added entries provided by the latest release of DrugBank are validated in two predicting tasks respectively.

Deep learning for drug‐drug interaction prediction: A comprehensive review

“…However, the authors believe that the removal of drugs without any known DDI during the experiment resulted in the model failing to identify DDI in the cleared drugs. In addition, for the problem mentioned above, SGRL-DDI [34] introduces Balance theory and Status theory in social networks to reveal the topological patterns between DDIs, which are modeled as signed and directed networks.…”

Fused Attentive Generative Adversarial Network based Personalized English Learning Material Recommendation System utilizing Knowledge Graph

“…In reference to the first issue raised above, examined features of English as linguistic instrument, knowledge features using an English course of particular grade as example. It then sorted out the structure of knowledge system, created a knowledge graph for course to represent knowledge points [13]. Regarding the second issue, this work examined the learning behaviour trajectories of the students in the grade, their user profiles, and the unique circumstances of online teaching platforms [14].…”