LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer
Long-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanisms of lncRNAs. Current computational methods for evaluating LPIs with their utility...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002132 |
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| author | Qin Lin Jie Sheng Chang Zhou Tao Xiao Yilei Meng Mingxin Lu Junfang Zhang Xueyun Yan Luying Peng Huaming Cao Li Li |
| author_facet | Qin Lin Jie Sheng Chang Zhou Tao Xiao Yilei Meng Mingxin Lu Junfang Zhang Xueyun Yan Luying Peng Huaming Cao Li Li |
| author_sort | Qin Lin |
| collection | DOAJ |
| description | Long-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanisms of lncRNAs. Current computational methods for evaluating LPIs with their utility and generalization have significant room for improvement. In this study, data splitting by incorporating protein clusters as group information reveals that lots of LPI prediction methods suffer from generalization flaws due to data leakage caused by ignoring LPI biological properties. To address the issue, we present LPItabformer, a tabular Transformer framework for predicting LPIs, that incorporates a domain shifts with uncertainty (DSU) module for generalization enhancement. The LPItabformer demonstrates a capacity to alleviate the generalization challenges associated with biases in LPI data and preferences in protein binding patterns. In addition, LPItabformer shows greater robustness and generalization on human and mouse LPI datasets compared to state-of-the-art methods. Ultimately, we have verified that the LPItabformer is capable of predicting novel LPIs. Code is available at https://github.com/Ci-TJ/LPItabformer. |
| format | Article |
| id | doaj-art-304c45ca1abe4e0cbba2e23cd1a58cb8 |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-304c45ca1abe4e0cbba2e23cd1a58cb82025-08-20T03:07:24ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01272323233510.1016/j.csbj.2025.05.050LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular TransformerQin Lin0Jie Sheng1Chang Zhou2Tao Xiao3Yilei Meng4Mingxin Lu5Junfang Zhang6Xueyun Yan7Luying Peng8Huaming Cao9Li Li10State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaTeaching Laboratory Center, Tongji University School of Medicine, Shanghai 200331, ChinaDepartment of Cardiology, Shibei Hospital, Shanghai 200435, ChinaState Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, ChinaDepartment of Cardiology, Shibei Hospital, Shanghai 200435, China; Correspondence author.State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China; Shanghai Arrhythmias Research Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China; Stem Cell Research Center, Medical School, Tongji University, Shanghai 200120, China; Corresponding author at: State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, ChinaLong-noncoding RNAs (LncRNAs) play important roles in physiological and pathological processes. Accurately predicting lncRNA-protein interactions (LPIs) is vital strategy for clarify functions and pathogenic mechanisms of lncRNAs. Current computational methods for evaluating LPIs with their utility and generalization have significant room for improvement. In this study, data splitting by incorporating protein clusters as group information reveals that lots of LPI prediction methods suffer from generalization flaws due to data leakage caused by ignoring LPI biological properties. To address the issue, we present LPItabformer, a tabular Transformer framework for predicting LPIs, that incorporates a domain shifts with uncertainty (DSU) module for generalization enhancement. The LPItabformer demonstrates a capacity to alleviate the generalization challenges associated with biases in LPI data and preferences in protein binding patterns. In addition, LPItabformer shows greater robustness and generalization on human and mouse LPI datasets compared to state-of-the-art methods. Ultimately, we have verified that the LPItabformer is capable of predicting novel LPIs. Code is available at https://github.com/Ci-TJ/LPItabformer.http://www.sciencedirect.com/science/article/pii/S2001037025002132Long non-coding RNALncRNA-protein interactionsTabular TransformerGeneralizationDeep learning |
| spellingShingle | Qin Lin Jie Sheng Chang Zhou Tao Xiao Yilei Meng Mingxin Lu Junfang Zhang Xueyun Yan Luying Peng Huaming Cao Li Li LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer Computational and Structural Biotechnology Journal Long non-coding RNA LncRNA-protein interactions Tabular Transformer Generalization Deep learning |
| title | LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer |
| title_full | LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer |
| title_fullStr | LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer |
| title_full_unstemmed | LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer |
| title_short | LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer |
| title_sort | lpitabformer enhancing generalization in predicting lncrna protein interactions via a tabular transformer |
| topic | Long non-coding RNA LncRNA-protein interactions Tabular Transformer Generalization Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025002132 |
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