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BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information
Zhan, ZH (Zhan, Zhao-Hui)[ 1 ]; Jia, LN (Jia, Li-Na)[ 2 ]; Zhou, Y (Zhou, Yong)[ 1 ]; Li, LP (Li, Li-Ping)[ 3 ]; Yi, HC (Yi, Hai-Cheng)[ 3 ]
2019
Source PublicationINTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
ISSN1422-0067
Volume20Issue:4Pages:1-14
Abstract

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.

KeywordncRNA-protein interaction bi-gram position specific scoring matrix k-mers deep learning
DOI10.3390/ijms20040978
Indexed BySCI
WOS IDWOS:000460805400186
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5730
Collection多语种信息技术研究室
Affiliation1.China Univ Min & Technol, Xuzhou 221116, Jiangsu, Peoples R China
2.Zaozhuang Univ, Coll Informat Sci & Engn, Zaozhuang 277100, Shandong, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
Recommended Citation
GB/T 7714
Zhan, ZH ,Jia, LN ,Zhou, Y ,et al. BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2019,20(4):1-14.
APA Zhan, ZH ,Jia, LN ,Zhou, Y ,Li, LP ,&Yi, HC .(2019).BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,20(4),1-14.
MLA Zhan, ZH ,et al."BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 20.4(2019):1-14.
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