ArticlePloS one2015
Identification of real microRNA precursors with a pseudo structure status composition approach.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 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
94 citing papers in PubMed, 242 citations in OpenAlex.
- Bioinformatics-driven genome-wide identification of viral miRNAs in high spillover bat coronaviruses and their target genes in human.Archives of microbiology · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
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- Deciphering the Role of Putative Novel miRNAs Encoded From the Newly Found Genomic Regions of T2T-CHM13 in the Progression of Collecting Duct Renal Cell Carcinoma.Cancer medicine · 2025Article
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- Predicting miRNA-disease associations based on PPMI and attention network.BMC bioinformatics · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
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- Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- Impact of Refractive Errors on the Academic Performance of High School Children of Lahore.Frontiers in public health · 2022Article
- BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models.Nucleic acids research · 2021Article
- Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands.RNA biology · 2021Article
- Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018.Frontiers in nutrition · 2021Article
- Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.Journal of cellular and molecular medicine · 2020Article
- LNRLMI: Linear neighbour representation for predicting lncRNA-miRNA interactions.Journal of cellular and molecular medicine · 2020Article
- A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.Current genomics · 2020Review
- Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction.Evolutionary bioinformatics online · 2020Article
- CirRNAPL: A web server for the identification of circRNA based on extreme learning machine.Computational and structural biotechnology journal · 2020Article
- iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties.International journal of molecular sciences · 2019Article
- A Linear Regression Predictor for Identifying NMolecular therapy. Nucleic acids · 2019Article
34 more citing papers are in PubMed but not listed here.
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 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called "iMcRNA-PseSSC" and "iMcRNA-ExPseSSC", were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.
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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.