ArticleEpigenetics & chromatin2020
Identification of DNA N
Article in Epigenetics & chromatin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Association of the YTHDF2 rs3738067 A>G variant with hepatoblastoma risk: a multicenter case-control study in Chinese children.BMC cancer · 2025Article
- LncLSTA: a versatile predictor unveiling subcellular localization of lncRNAs through long-short term attention.Bioinformatics advances · 2025Article
- Epigenetic Transgenerational Modifications Induced by Xenobiotic Exposure in Zebrafish.Frontiers in cell and developmental biology · 2022Review
- Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops.Frontiers in genetics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundAn increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N
resultsIn this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets.
conclusionsp6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.
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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.