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IFNg_DeepKG: A Novel Model for Identifying Interferon-Gamma-Inducing Epitopes Using Knowledge Graph RAG in Biomedical

Van The Le1, Juan Peter Timothy Yuune1, Yu-Yen Ou1,2

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|December 31, 2025
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A new deep learning framework, IFNg_DeepKG, enhances epitope prediction by integrating biological context with sequence data. This approach significantly improves the identification of interferon-gamma-inducing epitopes (IFNgIE) for vaccine and immunotherapy design.

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Area of Science:

  • Computational immunology
  • Bioinformatics
  • Machine learning in drug discovery

Background:

  • Accurate identification of interferon-gamma-inducing epitopes (IFNgIE) is crucial for designing effective vaccines and immunotherapies.
  • Current computational models often overlook essential biological context, limiting their predictive accuracy for epitope immunogenicity.

Purpose of the Study:

  • To develop a novel deep learning framework, IFNg_DeepKG, that integrates sequence-based patterns with rich biological context for improved IFNgIE prediction.
  • To enhance the design of next-generation vaccines and immunotherapies through more accurate epitope identification.

Main Methods:

  • IFNg_DeepKG framework combines a pretrained protein language model (ESM2), a custom knowledge graph (KG) with Retrieval-Augmented Generation (RAG), and a multiscale convolutional neural network (MSCNN).
  • The RAG-KG enriches sequence embeddings with external biological information (protein of origin, host, disease association) to improve immunogenicity predictions.

Main Results:

  • IFNg_DeepKG achieved superior performance on independent test datasets, with AUCs of 0.99 (Human H_IFNgInd1) and 0.95 (Mouse M_IFNgInd1).
  • Demonstrated strong cross-species generalization with AUCs of 0.94 (H_IFNgInd2) and 0.93 (M_IFNgInd2) on challenging datasets.
  • Successfully identified and classified clinically relevant epitopes associated with diseases like COVID-19 and Alzheimer's disease.

Conclusions:

  • IFNg_DeepKG significantly advances computational immunology by bridging sequence-based features and biological contexts.
  • The framework offers a scalable and powerful platform for rational epitope discovery, precision medicine, and the development of novel vaccines and immunotherapies.