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While several graph neural network-based methods have been proposed to infer ncRNA-drug resistance associations, they remain fundamentally constrained by semantic distortion induced by a sparse bipartite network and neglect of relational semantics among molecular entities, ultimately compromising both predictive reliability and biological interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we propose iNcRD-HG, a novel framework for identifying ncRNA-drug resistance associations. The framework addresses three critical aspects: constructing a context-enriched heterogeneous network that integrates six distinct molecular interaction types with bio-entity-specific attributes, developing a semantic-enhanced graph learning architecture that implements relation-type-aware message passing to capture complex contextual dependencies, and introducing an interpretability mechanism to reveal potential synergistic pathways underlying drug response. Experimental results demonstrate that iNcRD-HG achieves superior predictive performance across diverse benchmark datasets while deriving association features with strong discriminative capability. By identifying molecular synergistic contexts, iNcRD-HG provides mechanistically interpretable insights into ncRNA-mediated drug resistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Datasets and source codes are available at https:\/\/github.com\/Biohang\/iNcRD-HG.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag029","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T12:32:08Z","timestamp":1768307528000},"source":"Crossref","is-referenced-by-count":1,"title":["Semantic-enhanced heterogeneous graph learning for identifying ncRNAs associated with drug resistance"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-1716","authenticated-orcid":false,"given":"Hang","family":"Wei","sequence":"first","affiliation":[{"name":"School of Computer Science 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