ArticlePloS one2011
MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.Scientific reports · 2024Article
- Hepatitis B Virus and microRNAs: A Bioinformatics Approach.International journal of molecular sciences · 2023Review
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- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
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- A Novel miRNA Located in theBioMed research international · 2022Article
- PCV2 Regulates Cellular Inflammatory Responses through Dysregulating Cellular miRNA-mRNA Networks.Viruses · 2019Article
- Computational tools for plant small RNA detection and categorization.Briefings in bioinformatics · 2019Review
- Regulation of terpenoid biosynthesis by miRNA in Persicaria minor induced by Fusarium oxysporum.BMC genomics · 2019Article
- Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features.Scientific reports · 2019Article
- An Approach to Identify Individual Functional Single Nucleotide Polymorphisms and Isoform MicroRNAs.BioMed research international · 2019Article
- Comparing miRNA structure of mirtrons and non-mirtrons.BMC genomics · 2018Article
- Hemoglobin enhances miRNA-144 expression and autophagic activation mediated inflammation of microglia via mTOR pathway.Scientific reports · 2017Article
- On the performance of pre-microRNA detection algorithms.Nature communications · 2017Article
- miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.Bioinformatics (Oxford, England) · 2017Article
- Improving classification of mature microRNA by solving class imbalance problem.Scientific reports · 2016Article
- The impact of feature selection on one and two-class classification performance for plant microRNAs.PeerJ · 2016Article
- Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.Advances in bioinformatics · 2016Article
- miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.PloS one · 2015Article
- Computational Characterization of Exogenous MicroRNAs that Can Be Transferred into Human Circulation.PloS one · 2015Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundMicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates. METHODOLOGY/PRINCIPAL
findingsA miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method.
conclusionsThe superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/.
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