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

ABSTRACT: The accurate and efficient computational identification of interferon-gamma-inducing epitopes (IFNgIE) is a critical bottleneck in the design of next-generation vaccines and immunotherapies. Existing computational models, while adept at learning sequence-based patterns, frequently fail to incorporate the rich biological context that governs an epitope's immunogenicity, such as its protein of origin, host, and disease association. To address this limitation, we propose IFNg_DeepKG, a new deep learning framework that synergistically integrates a pretrained protein language model (ESM2), a custom knowledge graph (KG) using a Retrieval-Augmented Generation (RAG) approach, and a multiscale convolutional neural network (MSCNN). The model's central innovation lies in its use of the RAG-KG to enri

SUBMITTER: Le VT 

PROVIDER: S-EPMC12801304 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature