ArticleBMC bioinformatics2011
Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 45 citations in OpenAlex.
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- Potential molecular mechanism underlying cardiac fibrosis in diabetes mellitus: a narrative review.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2023Review
- A stacking ensemble deep learning approach to cancer type classification based on TCGA data.Scientific reports · 2021Article
- Computational methods for the ab initio identification of novel microRNA in plants: a systematic review.PeerJ. Computer science · 2019Article
- miR-133: A Suppressor of Cardiac Remodeling?Frontiers in pharmacology · 2018Review
- Probing an optimal class distribution for enhancing prediction and feature characterization of plant virus-encoded RNA-silencing suppressors.3 Biotech · 2016Article
- A framework for improving microRNA prediction in non-human genomes.Nucleic acids research · 2015Article
- SMOTE for high-dimensional class-imbalanced data.BMC bioinformatics · 2013Article
- Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles.PloS one · 2013Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately.
resultsIn the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively.
conclusionsNetwork parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.
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