ArticleFrontiers in physics2020
Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks.
Article in Frontiers in physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- DirectRM: integrated detection of landscape and crosstalk between multiple RNA modifications using direct RNA sequencing.Nature communications · 2025Article
- From beasts to bytes: Revolutionizing zoological research with artificial intelligence.Zoological research · 2023Review
- The emerging role of photoacoustic imaging in clinical oncology.Nature reviews. Clinical oncology · 2022Review
- The Dihydrouridine landscape from tRNA to mRNA: a perspective on synthesis, structural impact and function.RNA biology · 2022Review
- Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.Nature communications · 2021Article
- CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.BMC bioinformatics · 2021Article
- Review
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Epitranscriptome is an exciting area that studies different types of modifications in transcripts and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN) based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for eight epitranscriptome modifications, including m
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Registered trials
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