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  • EpiRanha: Hunting for Epitope Similarity with a Structure- and Residue . . .
    We introduce EpiRanha, a multimodal framework that integrates residue-level ESM-2 sequence embeddings with an E (n)-equivariant graph neural network operating on three-dimensional protein structure
  • EpiRanha: Hunting for Epitope Similarity with a Structure- and Residue . . .
    Here, we introduced EpiRanha, a hybrid sequence–structure framework that combines residue-level ESM-2 embeddings with an E(n)-equivariant graph network and a flexible beam-search matching algorithm
  • EpiRanha: Hunting for Epitope Similarity with a Structure- and Residue . . .
    We introduce EpiRanha , a multimodal framework that integrates residue-level ESM-2 sequence embeddings with an E (n)-equivariant graph neural network operating on three-dimensional protein structure
  • EpiRanha: Epitope Similarity Scoring with Graph Neural Network
    We introduce EpiRanha, a multimodal framework that integrates residue-level ESM-2 sequence embeddings with an E (n)-equivariant graph neural network operating on three-dimensional protein
  • Tygo Francissen - Semantic Scholar
    EpiRanha advances epitope characterization beyond sequence or geometry alone, enabling more robust off-target risk assessment, informing training-set construction for predictive models, and more selective antibody design
  • Weekly BioML Digest [April 27, 2026]
    Predicting RNA 3D structure and conformers using a pre-trained secondary structure model and structure-aware attention Wang, Wenkai, Peng, Zhenling, Yang, Jianyi — Nature Machine Intelligence, 2026-04-21
  • GitHub - biomed-AI GraphBepi
    GraphBepi is a novel graph-based method for accurate B-cell epitope prediction, which is able to capture spatial information using the predicted protein structures through the edge-enhanced deep graph neural network
  • Structural motif search across the protein-universe with Folddisco
    EpiRanha advances epitope characterization beyond sequence or geometry alone, enabling more robust off-target risk assessment, informing training-set construction for predictive models, and more selective antibody design
  • arXiv:2405. 20668v1 [q-bio. BM] 31 May 2024
    e build the structure encoder as follows First, we construct a 3D graph based on residue coordinates and add th ee different types of edges to the graph If the se-quential distance between the i-th residue and the j-th residue is below a predefined threshold dseq, the edge between these two
  • GitHub Pages - Jianan Zhao
    We propose GraphAny, the first fully-inductive node classification model that generalizes to any graph with arbitrary structure, feature and label spaces GraphAny surpasses supervised baselines (e g GCN, GAT) in an inductive manner





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