Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data | Zendy

Daiyun Huang | Zendy; Bowen Song | Zendy; Jingjue Wei | Zendy; Jionglong Su | Zendy; Frans Coenen | Zendy; Jia Meng | Zendy

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Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Author(s) -

Daiyun Huang,

Bowen Song,

Jingjue Wei,

Jionglong Su,

Frans Coenen,

Jia Meng

Publication year - 2021

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/btab278

Subject(s) - rna , computer science , computational biology , source code , identification (biology) , nucleic acid structure , resolution (logic) , data mining , machine learning , artificial intelligence , biology , gene , genetics , botany , operating system

Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available.

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