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    RPI-MDLStack: Predicting RNA–protein interactions through deep learning with stacking strategy and LASSO

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    RPIStack-08132021_Xin.pdf
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    Description:
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    Embargo End Date:
    2024-03-18
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    Embargo End Date:
    2024-03-18
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    Type
    Article
    Authors
    Yu, Bin cc
    Wang, Xue
    Zhang, Yaqun
    Gao, Hongli
    Wang, Yifei
    Liu, Yushuang
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
    Structural and Functional Bioinformatics Group
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    KAUST Grant Number
    FCC/1/1976-18-01
    REI/1/4216-01-01
    REI/1/4473-01-01
    REI/1/4742-01-01
    URF/1/4098-01-01
    URF/1/4379-01-0
    Date
    2022-03-18
    Embargo End Date
    2024-03-18
    Permanent link to this record
    http://hdl.handle.net/10754/676342
    
    Metadata
    Show full item record
    Abstract
    RNA–protein interactions (RPI) play a crucial role in foundational cellular physiological processes. Traditional methods to predict RPI are implemented through expensive and labor-intensive biological experiments, and existing computational methods are far from being satisfactory. There is a timely need for developing more cost-effective methods to predict RPI. A stacking ensemble deep learning-based framework (named RPI-MDLStack) is constructed for RPI prediction in this study. First, sequential-, physicochemical-, structural- and evolutionary-information from RNA and protein sequences are obtained through eight feature extraction methods. Then, the optimal feature is generated after eliminating the redundancy of the fusion features by the least absolute shrinkage and selection operator (LASSO). Based on the stacking strategy, the optimal feature is first learned by the base-classifier combination composed of multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), gated recurrent unit (GRU), and deep neural networks (DNN). Finally, the prediction scores are fed into a discriminative model for further training. The results of 5-fold cross-validation test prove the superior identification of RPI-MDLStack with accuracy of 96.7%, 87.3%, 94.6%, 97.1% and 89.5% on RPI488, RPI369, RPI2241, RPI1807, and RPI1446, respectively. Additionally, RPI-MDLStack obtained the overall prediction accuracy of 97.8% in the independent tests trained on RPI488. Compared with other state-of-the-art RPI prediction methods using the same datasets, RPI-MDLStack shows more robust and stable for predicting RPI.
    Citation
    Yu, B., Wang, X., Zhang, Y., Gao, H., Wang, Y., Liu, Y., & Gao, X. (2022). RPI-MDLStack: Predicting RNA–protein interactions through deep learning with stacking strategy and LASSO. Applied Soft Computing, 120, 108676. https://doi.org/10.1016/j.asoc.2022.108676
    Sponsors
    We thank anonymous reviewers for valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China (No. 62172248), the Natural Science Foundation of Shandong Province of China (No. ZR2021MF098), and the King Abdullah University of Science and Technology (KAUST) Office of Spon-sored Research (OSR) under award numbers (Nos. FCC/1/1976-18-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4379-01-01, REI/1/4742-01-01 and URF/1/4098-01-01)
    Publisher
    Elsevier BV
    Journal
    Applied Soft Computing
    DOI
    10.1016/j.asoc.2022.108676
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S156849462200148X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.asoc.2022.108676
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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