ArticleBioinformatics (Oxford, England)2021
Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- Elucidating the coordination of RNA processing using short-read and long-read RNA-sequencing methods.Nature reviews. Molecular cell biology · 2026Review
- Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites.BMC bioinformatics · 2026Article
- Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.BMC genomics · 2025Article
- Machine learning-augmented m6A-Seq analysis without a reference genome.Briefings in bioinformatics · 2025Article
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- m6A-TCPred: a web server to predict tissue-conserved human mBMC bioinformatics · 2024Article
- m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome.Nucleic acids research · 2024Article
- DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome.Frontiers in genetics · 2024Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- m6A-TSHub: Unveiling the Context-specific mGenomics, proteomics & bioinformatics · 2023Article
- RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.Nucleic acids research · 2023Article
- Pseudouridine Identification and Functional Annotation with PIANO.Methods in molecular biology (Clifton, N.J.) · 2023Article
- 4acCPred: Weakly supervised prediction ofMolecular therapy. Nucleic acids · 2022Article
- RNADSN: Transfer-Learning 5-Methyluridine (mInternational journal of molecular sciences · 2022Article
- Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.Nucleic acids research · 2022Article
- Article
- i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites ofFrontiers in genetics · 2022Article
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6 authors.
Funding
Abstract
motivationIncreasing 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.
resultsWe propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. AVAILABILITY IMPLEMENTATION: The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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