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November 14, 2025Scientific ReportsOpen Access

Revealing new associations between lncRNAs and diseases through cross attention mechanism and multiple level feature fusion

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Novel method identifies lncRNA-disease associations in complex diseases, implying improved insights through a cold-start approach.

The research aims to discover new associations between lncRNAs and diseases to aid in diagnosis and therapy.
Introduced a deep learning method called LDA-CAMF.
Developed a cross-attention mechanism for decoding lncRNA and disease relationships.
Employed multi-level feature fusion to enhance model performance.
Validated results using XGBoost and compared with six other methods.
Performed ablation studies to confirm predictive capabilities of LDA-CAMF.
Achieved highest AUCs of 0.9632 and 0.9759.
Obtained best AUPRs of 0.9369 and 0.9783 under 5-fold cross validation.
LDA-CAMF outperformed other methods in cold-start scenarios.
Identified associations such as HNF1A-AS1 with prostate cancer and HAR1A with diabetes.

Luo et al. (2025) studied this question.

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