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Song J, Tian S, Yu L, Yang Q, Dai Q, Wang Y, Wu W, Duan X. RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision. Math Biosci Eng 2022; 19:4749-4764. [PMID: 35430839 DOI: 10.3934/mbe.2022222] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.
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Affiliation(s)
- Jinmiao Song
- Department of Information Science and Engineering, Xinjiang University, Urumqi 830008, China
- Key Laboratory of Big Data Applied Technology, State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China
| | - Shengwei Tian
- Department of Software, Xinjiang University, Urumqi 830008, China
- Key Laboratory of Signal and Information Processing, Xinjiang University, Urumqi 830008, China
- Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830008, China
| | - Long Yu
- Department of Information Science and Engineering, Xinjiang University, Urumqi 830008, China
| | - Qimeng Yang
- Department of Information Science and Engineering, Xinjiang University, Urumqi 830008, China
| | - Qiguo Dai
- Key Laboratory of Big Data Applied Technology, State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China
| | - Yuanxu Wang
- Key Laboratory of Big Data Applied Technology, State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China
| | - Weidong Wu
- Center for Science Education, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China
| | - Xiaodong Duan
- Key Laboratory of Big Data Applied Technology, State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China
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