MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction

Disease-gene prediction (DGP) plays a pivotal role in understanding the genetic underpinnings of various diseases, offering insights for disease diagnosis, treatment, and prevention. Accurate identification of disease-related genes can enhance personalized medicine and the development of targeted therapies. While numerous methods for DGP have been proposed in the field, a significant challenge remains in effectively capturing and modeling the complex relationships among biological entities, such as diseases, symptoms, genes, and pathways. These intricate interactions are essential for learning robust representations of phenotypes and genotypes, which are critical for accurate DGP. In this study, we introduce MELGene, a knowledge-enhanced multimodel ensemble learning framework for DGP. MELGene leverages an adaptive integration of multiple pretrained knowledge inference models based on knowledge graph, effectively integrating the collective intelligence of diverse models to achieve more accurate gene predictions. The framework incorporates Model-aware Importance Learning, which dynamically adjusts the contributions of individual models, and introduces a dynamic ensemble mechanism to obtain robust consensus predictions. Finally, we conducted comprehensive experiments, including performance comparisons, which demonstrated the excellent performance of MELGene. Ablation experiments highlighted the positive impact of each module, while case studies showcased the reliability of the biological relevance of gastric, lung, and liver cancers, as supported by the analysis of network medicine, functional enrichment, and literature mining. MELGene offers a flexible framework for DGP through knowledge enhancement and adaptive ensemble learning, with broad potential for decoding disease mechanisms.