ArticleBMC genomics2018
Imbalance learning for the prediction of N
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed, 63 citations in OpenAlex.
- Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.BMC genomics · 2025Article
- Predicting microRNA target genes using pan-cancer correlation patterns.BMC genomics · 2025Article
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.Biology · 2024Review
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.Bioinformatics (Oxford, England) · 2023Article
- Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.Briefings in bioinformatics · 2023Review
- Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.International journal of molecular sciences · 2022Review
- Dynamic regulation and key roles of ribonucleic acid methylation.Frontiers in cellular neuroscience · 2022Review
- MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites ofFrontiers in microbiology · 2022Article
- M6A-BiNP: predicting NRNA biology · 2021Article
- Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.Nucleic acids research · 2021Article
- Epigenetics: Roles and therapeutic implications of non-coding RNA modifications in human cancers.Molecular therapy. Nucleic acids · 2021Review
- Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN).International journal of environmental research and public health · 2021Article
- Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study.International journal of environmental research and public health · 2021Article
- Article
- m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information.Frontiers in genetics · 2021Article
- LITHOPHONE: Improving lncRNA Methylation Site Prediction Using an Ensemble Predictor.Frontiers in genetics · 2020Article
- Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics.Computational and structural biotechnology journal · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundN
resultsWe propose to use cost-sensitive learning ideas to resolve the imbalance data issues in the human mRNA m
conclusionThe proposed cost-sensitive model and the new features are useful in human mRNA m
Indexed as
Identifiers
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.