CPDT: A Novel Cluster-based Paired Decision Tree for Identifying Biomedical Entity Interactions

For the interaction prediction task in the biomedical field, most machine learning algorithms overlook the relationships between entities within a pair by treating their features independently. To address this issue, this paper proposes a novel Cluster-based Paired Decision Tree model (CPDT), which pairs synonymous features of entity pairs to form paired feature spaces for simultaneous processing. It employs an adaptive grid-based clustering algorithm to partition these spaces in an axis-parallel manner, constructing interpretable decision boundaries. Moreover, the clustering algorithm leverages the probability density function to accommodate various data distributions in paired feature spaces, enhancing the effectiveness of sample partitioning. Experimental results demonstrate that CPDT performs well in two interaction prediction tasks: Drug Combination and Synthetic Lethality predictions. Furthermore, CPDT yields simple and interpretable decision rules that uncover potential patterns in biomedical interaction prediction. It also identifies molecules with medical significance, suggesting promising applications in the biomedical domain.