How could a weighted drug-drug network help improve adverse drug reaction predictions? Machine learning reveals the importance of edge weights

Recent years have witnessed the booming data of drugs and their associated adverse drug reactions (ADRs), resulting in a comparatively high hospitalization rate worldwide. Therefore, a tremendous amount of research has been done to predict ADRs to keep the risks at a minimum. Due to the nature of the lab experiments being costly and time-consuming, researchers are looking forward to more extensive use of data mining and machine learning techniques in this field. This paper constructs a weighted drug-drug network based on an integration of various data sources. The network presents underlying relationships between drugs by creating connections between them according to their common ADRs. Then multiple node-level and graph-level network features are extracted from this network, e.g. weighted degree centrality, weighted PageRanks etc. By concatenating these features to the original drug features, it could be made possible to train and test seven classical machine learning algorithms, e.g. Logistic Regression, Random Forest, Support Vector Machine, etc. The experiments conclude that all the tested machine learning methods would benefit from adding those network measures, and the logistic regression (LR) model provides the highest mean AUROC score (0.821) across all ADRs in the experiment. Weighted degree centrality and weighted PageRanks are identified to be the most important network features in the LR classifier. These shreds of evidence strongly support that the network approach could be fundamental in future ADR prediction, and the network edge weights are important in the logistic regression model.

Automated summary

In recent years, there has been an exponential growth in drug-related data and adverse drug reactions (ADRs), leading to a comparatively high hospitalization rate worldwide. To minimize risks, extensive research has been conducted to predict ADRs. Due to the high cost and time-consuming nature of lab experiments, researchers are exploring the use of data mining and machine learning techniques in this field. This paper constructs a weighted drug-drug network by integrating various data sources, revealing underlying relationships between drugs based on common ADRs. Network features are extracted from this network, such as weighted degree centrality and weighted PageRanks, which are concatenated with original drug features to train and test seven classical machine learning algorithms. Experiment results show that adding these network measures benefits all tested machine learning methods, with logistic regression achieving the highest mean AUROC score (0.821) across all ADRs. Weighted degree centrality and weighted PageRanks are identified as the most important network features in the logistic regression classifier. This evidence strongly supports the fundamental role of the network approach in future ADR prediction, where network edge weights play a crucial role in the logistic regression model.