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

Haoyu Tian1, Kuo Yang1, Zeyu Liu1

  • 1Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing, 100044, China.

Briefings in Bioinformatics
|April 15, 2026
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MELGene, a novel framework, improves disease-gene prediction by integrating multiple models using knowledge graphs. This approach enhances understanding of genetic disease links for personalized medicine and targeted therapies.

Keywords:
Disease–gene predictionensemble learningknowledge graph completion

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Disease-gene prediction (DGP) is crucial for understanding genetic disease factors, aiding diagnosis, treatment, and personalized medicine.
  • Existing DGP methods struggle to model complex interactions between diseases, symptoms, genes, and pathways effectively.
  • Robust modeling of these intricate biological relationships is key for accurate phenotype and genotype representation in DGP.

Purpose of the Study:

  • To introduce MELGene, a knowledge-enhanced multimodel ensemble learning framework designed for accurate disease-gene prediction.
  • To leverage knowledge graphs and adaptive ensemble learning for improved DGP accuracy.
  • To demonstrate the framework's effectiveness in capturing complex biological interactions for enhanced gene predictions.

Main Methods:

  • Developed MELGene, a framework integrating multiple pretrained knowledge inference models via knowledge graphs.
  • Implemented Model-aware Importance Learning for dynamic adjustment of individual model contributions.
  • Utilized a dynamic ensemble mechanism to generate robust consensus predictions.

Main Results:

  • MELGene demonstrated excellent performance in comprehensive experimental comparisons.
  • Ablation experiments confirmed the positive contribution of each framework module.
  • Case studies on gastric, lung, and liver cancers validated the biological relevance of predictions through network medicine and literature mining.

Conclusions:

  • MELGene provides a flexible and effective framework for disease-gene prediction through knowledge enhancement and adaptive ensemble learning.
  • The framework shows significant potential for advancing the understanding of disease mechanisms and supporting personalized medicine.
  • MELGene's approach offers a powerful tool for decoding complex genetic underpinnings of diseases.