ArticleNucleic acids research2021
Modeling multi-species RNA modification through multi-task curriculum learning.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Structure-aware deep learning enhances m6A prediction and reveals cell type-associated RNA structural signatures.PLoS computational biology · 2026Article
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- mArXiv · 2026Article
- Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites.BMC bioinformatics · 2026Article
- Interpretability-guided RNA NCommunications biology · 2025Article
- Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning.Nucleic acids research · 2025Article
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- m6A-TCPred: a web server to predict tissue-conserved human mBMC bioinformatics · 2024Article
- Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.Bioinformatics (Oxford, England) · 2023Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods.Current genomics · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- m6A-TSHub: Unveiling the Context-specific mGenomics, proteomics & bioinformatics · 2023Article
- Review
- m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.Frontiers in microbiology · 2023Article
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- 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
- Review
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7 authors.
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Abstract
N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, various experimental techniques and computational methods have been developed to characterize the transcriptome-wide landscapes of m6A modification for understanding its underlying mechanisms and functions in mRNA regulation. However, the experimental techniques are generally costly and time-consuming, while the existing computational models are usually designed only for m6A site prediction in a single-species and have significant limitations in accuracy, interpretability and generalizability. Here, we propose a highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously. Extensive computational experiments demonstrate the superior performances of MASS when compared to the state-of-the-art prediction methods. Furthermore, the contextual sequence features of m6A captured by MASS can be explained by the known critical binding motifs of the related RNA-binding proteins, which also help elucidate the similarity and difference among m6A features across species. In addition, based on the predicted m6A profiles, we further delineate the relationships between m6A and various properties of gene regulation, including gene expression, RNA stability, translation, RNA structure and histone modification. In summary, MASS may serve as a useful tool for characterizing m6A modification and studying its regulatory code. The source code of MASS can be downloaded from https://github.com/mlcb-thu/MASS.
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