Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting
Abstract lncRNAs are densely related to many human diseases. Identifying new lncRNA-disease associations (LDAs) conduces to better deciphering mechanisms of diseases, finding new biomarkers, and further promoting their diagnosis and treatment. In this manuscript, we devise an LDA prediction framewor...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-03269-0 |
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| author | Lili Tang Liangliang Huang Yi Yuan |
| author_facet | Lili Tang Liangliang Huang Yi Yuan |
| author_sort | Lili Tang |
| collection | DOAJ |
| description | Abstract lncRNAs are densely related to many human diseases. Identifying new lncRNA-disease associations (LDAs) conduces to better deciphering mechanisms of diseases, finding new biomarkers, and further promoting their diagnosis and treatment. In this manuscript, we devise an LDA prediction framework called LDA-GARB. LDA-GARB first combines nonnegative matrix factorization to extract linear features of lncRNAs and diseases. Next, it computes lncRNA similarity and disease similarity and adopts a graph autoencoder to extract nonlinear features of lncRNAs and diseases. Subsequently, the extracted features are concatenated as a vector. Finally, it takes the obtained vector as inputs and designs a noise-robust gradient boosting model to uncover potential associations from unknown lncRNA-disease pairs. To investigate the LDA-GARB performance, we used precision, recall, accuracy, F1-score, AUC, and AUPR as measurement metrics and performed multiple comparison experiments. First, it was benchmarked with four representative LDA prediction methods, i.e., SDLDA, LDNFSGB, LDAenDL, and LDA-VGHB, under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs. Next, it was compared with four representative boosting models, i.e., XGBoost, AdaBoost, CatBoost, and LightGBM, under the above three different cross validations. Subsequently, the performance of LDA-GARB against LDA-LNSUBRW, GAMCLDA, LDA-VGHB, LDAGM, and GANLDA on imbalanced data was evaluated. We also performed parameter sensitivity analysis and ablation experiments. The results demonstrated that LDA-GARB improved LDA prediction. Finally, LDA-GARB was applied to predict potential associated lncRNAs for colorectal cancer and breast cancer. CCDC26 and HAR1A have been inferred to have an association with the two cancers, respectively. As a useful LDA identification tool, LDA-GARB is freely available at https://github.com/smiling199/LDA-GARB . |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-a42d61df3d5e4cebb057941ba442ff5d2025-08-20T04:02:46ZengNature PortfolioScientific Reports2045-23222025-05-0115112310.1038/s41598-025-03269-0Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boostingLili Tang0Liangliang Huang1Yi Yuan2School of Computer Science, Hunan University of TechnologySchool of Information Technology and Administration, Hunan University of Finance and EconomicsSchool of Computer Science, Hunan University of TechnologyAbstract lncRNAs are densely related to many human diseases. Identifying new lncRNA-disease associations (LDAs) conduces to better deciphering mechanisms of diseases, finding new biomarkers, and further promoting their diagnosis and treatment. In this manuscript, we devise an LDA prediction framework called LDA-GARB. LDA-GARB first combines nonnegative matrix factorization to extract linear features of lncRNAs and diseases. Next, it computes lncRNA similarity and disease similarity and adopts a graph autoencoder to extract nonlinear features of lncRNAs and diseases. Subsequently, the extracted features are concatenated as a vector. Finally, it takes the obtained vector as inputs and designs a noise-robust gradient boosting model to uncover potential associations from unknown lncRNA-disease pairs. To investigate the LDA-GARB performance, we used precision, recall, accuracy, F1-score, AUC, and AUPR as measurement metrics and performed multiple comparison experiments. First, it was benchmarked with four representative LDA prediction methods, i.e., SDLDA, LDNFSGB, LDAenDL, and LDA-VGHB, under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs. Next, it was compared with four representative boosting models, i.e., XGBoost, AdaBoost, CatBoost, and LightGBM, under the above three different cross validations. Subsequently, the performance of LDA-GARB against LDA-LNSUBRW, GAMCLDA, LDA-VGHB, LDAGM, and GANLDA on imbalanced data was evaluated. We also performed parameter sensitivity analysis and ablation experiments. The results demonstrated that LDA-GARB improved LDA prediction. Finally, LDA-GARB was applied to predict potential associated lncRNAs for colorectal cancer and breast cancer. CCDC26 and HAR1A have been inferred to have an association with the two cancers, respectively. As a useful LDA identification tool, LDA-GARB is freely available at https://github.com/smiling199/LDA-GARB .https://doi.org/10.1038/s41598-025-03269-0 |
| spellingShingle | Lili Tang Liangliang Huang Yi Yuan Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting Scientific Reports |
| title | Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting |
| title_full | Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting |
| title_fullStr | Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting |
| title_full_unstemmed | Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting |
| title_short | Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting |
| title_sort | predicting lncrna and disease associations with graph autoencoder and noise robust gradient boosting |
| url | https://doi.org/10.1038/s41598-025-03269-0 |
| work_keys_str_mv | AT lilitang predictinglncrnaanddiseaseassociationswithgraphautoencoderandnoiserobustgradientboosting AT lianglianghuang predictinglncrnaanddiseaseassociationswithgraphautoencoderandnoiserobustgradientboosting AT yiyuan predictinglncrnaanddiseaseassociationswithgraphautoencoderandnoiserobustgradientboosting |