RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision
In: Mathematical Biosciences and Engineering, Jg. 19 (2022), Heft 5, S. 4749-4764
Online
academicJournal
Zugriff:
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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RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision
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Autor/in / Beteiligte Person: | Song, Jinmiao ; Tian, Shengwei ; Yu, Long ; Yang, Qimeng ; Dai, Qiguo ; Wang, Yuanxu ; Wu, Weidong ; Duan, Xiaodong |
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Zeitschrift: | Mathematical Biosciences and Engineering, Jg. 19 (2022), Heft 5, S. 4749-4764 |
Veröffentlichung: | AIMS Press, 2022 |
Medientyp: | academicJournal |
ISSN: | 1551-0018 (print) ; 1523-1879 (print) |
DOI: | 10.3934/mbe.2022222?viewType=HTML |
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