ArticleCommunications biology2022
Predicting genes associated with RNA methylation pathways using machine learning.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- NSUN6-Mediated m5C Modification of OAS2 Is Implicated in Neuronal PANoptosis during Perioperative Neurocognitive Disorders.Molecular neurobiology · 2026Article
- A network medicine framework for multi-modal data integration in therapeutic target discovery.Communications chemistry · 2026Article
- MCLCBA: multi-view contrastive learning network for RNA methylation site prediction.BMC bioinformatics · 2025Article
- Review
- RNA modifications and their role in gene expression.Frontiers in molecular biosciences · 2025Review
- Data augmentation based on the WGAN-GP with data block to enhance the prediction of genes associated with RNA methylation pathways.Scientific reports · 2024Article
- The rise of epitranscriptomics: recent developments and future directions.Trends in pharmacological sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets. The best models reached 88% accuracy based on cross-validation, and an average 91% accuracy on the test set. Using protein-protein interaction data, we propose six molecular sub-networks linking model predictions to previously known RNA methylation genes, with roles in mRNA methylation, tRNA processing, rRNA processing, but also protein and chromatin modifications. Our study exemplifies how access to large omics datasets joined by machine learning methods can be used to predict gene function.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.