Articlenpj biomedical innovations2026
Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway heterogeneous knowledge graph.
Article in npj biomedical innovations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
Abstract
Conventional histopathological analysis focuses on single-gene mutations and struggles to capture pathway-level dysregulation driving cancer. To address this, we propose LCG-HGNN, a Logic-Constrained Gene-Pathway Heterogeneous Graph Neural Network that enables collaborative recognition of gene groups and infers signaling pathway alterations from whole-slide images. By integrating a gene-pathway graph structure, dynamic edge weighting within our proposed KePathGraph framework, which also incorporates logical clauses, our framework achieves superior prediction accuracy and clinical interpretability over single-gene and multi-label baselines. This work establishes a pathway-oriented paradigm for histopathological interpretation, providing deeper insights into the mechanisms underlying cancer initiation and progression.
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Registered trials
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