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RNADSN: Transfer-Learning 5-Methyluridine (m 5 U) Modification on mRNAs from Common Features of tRNA.
- Published in:
- International Journal of Molecular Sciences, 2022, v. 23, n. 21, p. 13493, doi. 10.3390/ijms232113493
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- Article
Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.
- Published in:
- Nature Communications, 2021, v. 12, p. 1, doi. 10.1038/s41467-021-24313-3
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- Article
Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data.
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- Bioinformatics, 2021, v. 37, p. i222, doi. 10.1093/bioinformatics/btab278
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- Article
Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis.
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- Biomolecules (2218-273X), 2022, v. 12, n. 7, p. N.PAG, doi. 10.3390/biom12070906
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- Article
Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes.
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- Briefings in Bioinformatics, 2023, v. 24, n. 3, p. 1, doi. 10.1093/bib/bbad105
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- Article
RC-Net: Regression Correction for End-To-End Chromosome Instance Segmentation.
- Published in:
- Frontiers in Genetics, 2022, v. 13, p. 1, doi. 10.3389/fgene.2022.895099
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- Article
m<sup>6</sup>A-Atlas v2.0: updated resources for unraveling the N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) epitranscriptome among multiple species.
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- Nucleic Acids Research, 2024, v. 52, n. D1, p. D194, doi. 10.1093/nar/gkad691
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- Article
RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.
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- Nucleic Acids Research, 2023, v. 51, n. D1, p. D1388, doi. 10.1093/nar/gkac750
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- Article
Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.
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- Nucleic Acids Research, 2022, v. 50, n. 18, p. 10290, doi. 10.1093/nar/gkac830
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- Article
m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome.
- Published in:
- Nucleic Acids Research, 2022, v. 50, n. D1, p. D196, doi. 10.1093/nar/gkab1075
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- Article
Developing a Semi-Supervised Approach Using a PU-Learning-Based Data Augmentation Strategy for Multitarget Drug Discovery.
- Published in:
- International Journal of Molecular Sciences, 2024, v. 25, n. 15, p. 8239, doi. 10.3390/ijms25158239
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- Publication type:
- Article