LncRNAnet: long non-coding RNA identification using deep learning | Zendy

Junghwan Baek | Zendy; Byunghan Lee | Zendy; Sunyoung Kwon | Zendy; Sungroh Yoon | Zendy

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LncRNAnet: long non-coding RNA identification using deep learning

Author(s) -

Junghwan Baek,

Byunghan Lee,

Sunyoung Kwon,

Sungroh Yoon

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty418

Subject(s) - computer science , computational biology , convolutional neural network , deep learning , artificial intelligence , identification (biology) , coding (social sciences) , open reading frame , frame (networking) , machine learning , transcriptome , sequence (biology) , artificial neural network , long non coding rna , rna , biology , genetics , gene , gene expression , telecommunications , statistics , botany , mathematics , peptide sequence

Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biological studies. The development of next-generation sequencing has helped in the discovery of lncRNA transcripts. However, the experimental verification of numerous transcriptomes is time consuming and costly. To alleviate these issues, a computational approach is needed to distinguish lncRNAs from the transcriptomes.

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