lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning
Autor: | Shao-Wu Zhang, Jinjie Ni, Xiao-Nan Fan, Song-Yao Zhang |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0301 basic medicine
multimodal learning Computational biology alignment-free Biology Catalysis Article Inorganic Chemistry lcsh:Chemistry 03 medical and health sciences Mice Deep Learning Animals Humans long noncoding RNA Physical and Theoretical Chemistry Molecular Biology lcsh:QH301-705.5 Spectroscopy Protein coding 030102 biochemistry & molecular biology Mechanism (biology) business.industry Deep learning Organic Chemistry General Medicine Long non-coding RNA Computer Science Applications Multimodal learning 030104 developmental biology lcsh:Biology (General) lcsh:QD1-999 RNA Long Noncoding Artificial intelligence business Databases Nucleic Acid Software Coding (social sciences) |
Zdroj: | International Journal of Molecular Sciences Volume 21 Issue 15 International Journal of Molecular Sciences, Vol 21, Iss 5222, p 5222 (2020) |
ISSN: | 1422-0067 |
Popis: | Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs. The source code could be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep. |
Databáze: | OpenAIRE |
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