LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting
Abstract Background Long non-coding RNAs (lncRNAs) play essential roles in various physiological and pathological processes. Inferring new lncRNA-disease associations (LDAs) not only promotes us to better understand these complex biological processes, but also provides new options for the diagnosis...
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06169-2 |
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| author | Chengqiu Dai Linna Wang Yingwei Deng Xuzhu Gao Jingyu Zhang |
| author_facet | Chengqiu Dai Linna Wang Yingwei Deng Xuzhu Gao Jingyu Zhang |
| author_sort | Chengqiu Dai |
| collection | DOAJ |
| description | Abstract Background Long non-coding RNAs (lncRNAs) play essential roles in various physiological and pathological processes. Inferring new lncRNA-disease associations (LDAs) not only promotes us to better understand these complex biological processes, but also provides new options for the diagnosis and prevention of diseases. Results A novel computational model, LDA-SCGB, is proposed to predict new LDAs. LDA-SCGB first extracts features of each lncRNA-disease pair with singular value decomposition. Next, it classifies unknown lncRNA-disease pairs through the condensed gradient boosting model. The results demonstrated that LDA-SCGB greatly outperformed the other four representative LDA inference methods (SDLDA, LDNFSGB, LDAenDL and LDASR) under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs on three LDA datasets, which were from lncRNADisease v2.0, MNDR, and lncRNADisease v3.0, respectively. LDA-SCGB was further used to find potential lncRNAs for colorectal cancer, heart failure, and lung adenocarcinoma. The results demonstrated that CCDC26, MIAT, and CCDC26 had higher association probability with colorectal cancer, heart failure, and lung adenocarcinoma, respectively. Conclusions We foresee that LDA-SCGB was capable of predicting potential lncRNAs for complex diseases and further assisting in cancer diagnosis and therapy |
| format | Article |
| id | doaj-art-e401532295634b659fe3061de0439894 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-e401532295634b659fe3061de04398942025-08-20T03:06:36ZengBMCBMC Bioinformatics1471-21052025-07-0126112710.1186/s12859-025-06169-2LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boostingChengqiu Dai0Linna Wang1Yingwei Deng2Xuzhu Gao3Jingyu Zhang4School of Computer Science and Engineering, Hunan Institute of TechnologyThe Sixth Department of Oncology, Beidahuang Industry Group General Hospital (Heilongjiang Second Cancer Hospital)School of Computer Science and Engineering, Hunan Institute of TechnologyInstitute of Clinical Oncology, The Second People’s Hospital of Lianyungang City (Cancer Hospital of Lianyungang)Department of Oncology, The Second People’s Hospital of Lianyungang city (Cancer Hospital of Lianyungang), Lianyungang Hospital Affiliated to Kangda College of Nanjing Medical UniversityAbstract Background Long non-coding RNAs (lncRNAs) play essential roles in various physiological and pathological processes. Inferring new lncRNA-disease associations (LDAs) not only promotes us to better understand these complex biological processes, but also provides new options for the diagnosis and prevention of diseases. Results A novel computational model, LDA-SCGB, is proposed to predict new LDAs. LDA-SCGB first extracts features of each lncRNA-disease pair with singular value decomposition. Next, it classifies unknown lncRNA-disease pairs through the condensed gradient boosting model. The results demonstrated that LDA-SCGB greatly outperformed the other four representative LDA inference methods (SDLDA, LDNFSGB, LDAenDL and LDASR) under 5-fold cross validations on lncRNAs, diseases, and lncRNA-disease pairs on three LDA datasets, which were from lncRNADisease v2.0, MNDR, and lncRNADisease v3.0, respectively. LDA-SCGB was further used to find potential lncRNAs for colorectal cancer, heart failure, and lung adenocarcinoma. The results demonstrated that CCDC26, MIAT, and CCDC26 had higher association probability with colorectal cancer, heart failure, and lung adenocarcinoma, respectively. Conclusions We foresee that LDA-SCGB was capable of predicting potential lncRNAs for complex diseases and further assisting in cancer diagnosis and therapyhttps://doi.org/10.1186/s12859-025-06169-2LncRNA-disease associationCondensed gradient boostingColorectal cancerHeart failure |
| spellingShingle | Chengqiu Dai Linna Wang Yingwei Deng Xuzhu Gao Jingyu Zhang LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting BMC Bioinformatics LncRNA-disease association Condensed gradient boosting Colorectal cancer Heart failure |
| title | LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting |
| title_full | LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting |
| title_fullStr | LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting |
| title_full_unstemmed | LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting |
| title_short | LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting |
| title_sort | lda scgb inferring lncrna disease associations based on condensed gradient boosting |
| topic | LncRNA-disease association Condensed gradient boosting Colorectal cancer Heart failure |
| url | https://doi.org/10.1186/s12859-025-06169-2 |
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