ncRFP: A Novel end-to-end Method for Non-Coding RNAs Family Prediction Based on Deep Learning

Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is he...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2021-03, Vol.18 (2), p.784-789
Main Authors: Wang, Linyu, Zheng, Shaoge, Zhang, Hao, Qiu, Zhiyang, Zhong, Xiaodan, Liuliu, Haiming, Liu, Yuanning
Format: Article
Language:English
Subjects:
Biological activity
Deep learning
Encoding
end-to-end
Feature extraction
Logic gates
ncRFP
ncRNAs family
ncRNAs sequences
Non-coding RNA
Pathogenesis
Periodic structures
Predictions
Protein structure
RNA
Secondary structure
Source code
Task analysis
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Summary:Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is helpful to the research of their function, it is of necessity and urgency to predict the family of each ncRNAs. Although several traditional excellent methods are applicable to predict the family of ncRNAs, their complex procedures or inaccurate performance remain major problems confronting us. The main idea of those methods is first to predict the secondary structure, and then identify ncRNAs family according to properties of the secondary structure. Unfortunately, the multi-step error superposition, especially the imperfection of RNA secondary structure prediction tools, maybe the cause of low accuracy. In this paper, a novel end-to-end method 'ncRFP' was proposed to complete the prediction task based on Deep Learning. Instead of predicting the secondary structure, ncRFP predicts the ncRNAs family by automatically extracting features from ncRNAs sequences. Compared with other methods, ncRFP not only simplifies the process but also improves accuracy. The source code of ncRFP can be available at https://github.com/linyuwangPHD/ncRFP .
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2020.2982873