ArticleRNA biology2021
HSM6AP: a high-precision predictor for the Homo
Article in RNA biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 30 citations in OpenAlex.
- A novel method for the identification and quantification of N6-methyladenosine motifs in RNA transcripts.Molecular biology reports · 2026Article
- mArXiv · 2026Article
- Comprehensive Profiling of NAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From Detection to Prediction: Advances in m6A Methylation Analysis Through Machine Learning and Deep Learning with Implications in Cancer.International journal of molecular sciences · 2025Review
- PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms.Frontiers in genetics · 2024Article
- Insights into N6-methyladenosine (m6A) modification of noncoding RNA in tumor microenvironment.Aging · 2023Review
- Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning.Frontiers in genetics · 2023Article
- Crosstalk between N6-methyladenosine (m6A) modification and noncoding RNA in tumor microenvironment.International journal of biological sciences · 2023Review
- Article
- MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites ofFrontiers in microbiology · 2022Article
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- M6A-BiNP: predicting NRNA biology · 2021Article
- The functional roles, cross-talk and clinical implications of m6A modification and circRNA in hepatocellular carcinoma.International journal of biological sciences · 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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the
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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 Socratic graph.