Next Article in Journal
Genome-Wide Detection of Major and Epistatic Effect QTLs for Seed Protein and Oil Content in Soybean Under Multiple Environments Using High-Density Bin Map
Previous Article in Journal
Compositional Features of HDL Particles Interact with Albuminuria to Modulate Cardiovascular Disease Risk
Previous Article in Special Issue
Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs
Open AccessArticle

BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

1
China University of Mining and Technology, Xuzhou 221116, China
2
College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, Shandong, China
3
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(4), 978; https://doi.org/10.3390/ijms20040978
Received: 1 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2018)
The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research. View Full-Text
Keywords: ncRNA-protein interaction; bi-gram; position specific scoring matrix; k-mers; deep learning ncRNA-protein interaction; bi-gram; position specific scoring matrix; k-mers; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Zhan, Z.-H.; Jia, L.-N.; Zhou, Y.; Li, L.-P.; Yi, H.-C. BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information. Int. J. Mol. Sci. 2019, 20, 978.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop