A machine learning framework for interpreting phylogenetic tree patterns in interkingdom horizontal gene transfer.

Introduction: Horizontal gene transfer (HGT), the movement of genetic material between unrelated organisms, is widely recognized as an important driver of genome evolution in bacteria. In eukaryotes, however, the evolutionary impact of HGT remains debated. The identification of interkingdom HGT (iHGT) is especially challenging due to the lack of gold standard methods. While automated approaches for iHGT candidate detection exist, the interpretation of phylogenetic tree topologies into biologically meaningful evolutionary patterns has traditionally depended on expert manual inspection, a process that is subjective, difficult to reproduce, and not scalable to large datasets. Methods: We present a computational framework that formalizes phylogenetic tree interpretation as a supervised machine-learning problem. We define five recurrent phylogenetic patterns (iHGT, NoHGT, Limited donor evidence, Multiple major clades, and Patchy phylogeny) and developed a feature-extraction pipeline that captures taxonomic composition and phylogenetic topology through six biologically interpretable descriptors derived from unrooted gene trees. Several machine-learning algorithms were evaluated using repeated stratified cross-validation, and model interpretability was assessed through permutation importance, SHAP analysis, rule-based baselines, and feature ablation. Performance was further validated on simulated datasets, real biological iHGT candidates, and an independently annotated external dataset. Results: A Random Forest (RF) classifier achieved the best performance (AUC-ROC = 0.98; accuracy = 0.89). Topological distance and lineage-distribution features were identified as the strongest contributors to classification performance. Feature ablation and rule-based baseline comparisons demonstrated that model performance cannot be explained solely by explicit annotation rules and benefits from combining clade-composition and topology-based information. The RF classifier showed low misclassification rates on simulated and real biological datasets (7.8% and 10.43%, respectively), showed substantial agreement (69.4%) with an independently annotated external dataset, and consistently outperformed AVP (Alienness vs. Predictor), Alien Index, and HGT Index across all evaluated metrics. Conclusion: These results support the use of machine learning for the automated, reproducible, and scalable classification of expert-defined phylogenetic patterns. The framework also highlights that some topologies commonly interpreted as evidence of iHGT may reflect alternative evolutionary processes, emphasizing the need for cautious and context-aware inference.