ArticleBMC bioinformatics2015
ViralmiR: a support-vector-machine-based method for predicting viral microRNA precursors.
Article in BMC bioinformatics, 2015. 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.
- Comparative analysis of sequential and thermodynamic features of pre-miRNA in insects with various organisms and applying XGBoost for one-vs-rest binary classification.Scientific reports · 2025Article
- Article
- MicroRNA Targeting.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Identification and host response interaction study of SARS-CoV-2 encoded miRNA-like sequences: an in silico approach.Computers in biology and medicine · 2021Article
- Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.Scientific reports · 2021Article
- Profile of Small RNAs, vDNA Forms and Viral Integrations in Late Chikungunya Virus Infection ofViruses · 2021Article
- Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity.Molecular biology research communications · 2020Article
- Computational prediction of miRNAs in Nipah virus genome reveals possible interaction with human genes involved in encephalitis.Molecular biology research communications · 2018Article
- A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images.Behavioural neurology · 2016Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundmicroRNAs (miRNAs) play a vital role in development, oncogenesis, and apoptosis by binding to mRNAs to regulate the posttranscriptional level of coding genes in mammals, plants, and insects. Recent studies have demonstrated that the expression of viral miRNAs is associated with the ability of the virus to infect a host. Identifying potential viral miRNAs from experimental sequence data is valuable for deciphering virus-host interactions. Thus far, a specific predictive model for viral miRNA identification has yet to be developed. METHODS AND
resultsHere, we present ViralmiR for identifying viral miRNA precursors on the basis of sequencing and structural information. We collected 263 experimentally validated miRNA precursors (pre-miRNAs) from 26 virus species and generated sequencing fragments from virus and human genomes as the negative dataset. Support vector machine and random forest models were established using 54 features from RNA sequences and secondary structural information. The results show that ViralmiR achieved a balanced accuracy higher than 83%, which is superior to that of previously developed tools for identifying pre-miRNAs.
conclusionsThe easy-to-use ViralmiR web interface has been provided as a helpful resource for researchers to use in analyzing and deciphering virus-host interactions. The web interface of ViralmiR can be accessed at http://csb.cse.yzu.edu.tw/viralmir/.
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