ArticleJournal of translational medicine2013
VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses.
Article in Journal of translational medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- HMPV-TherResDB: Comprehensive human metapneumovirus (HMPV) database for sequence-structure annotations, vaccine resources, and therapeutics research.Virus research · 2026Article
- SiaRNA: a siamese neural network with bidirectional cross-attention for pairwise siRNA-mRNA efficacy prediction.Frontiers in bioinformatics · 2026Article
- Therapeutic Approaches of Viral Gene Silencing by Small Interfering RNA: Strategies to Prevent the Emergence of Antiviral Resistant Escape Mutants.Pharmaceuticals (Basel, Switzerland) · 2025Review
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- siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.Briefings in bioinformatics · 2024Article
- Exploring the challenges of RNAi-based strategies for crop protection.Advanced biotechnology · 2024Review
- Potential of siRNA in COVID-19 therapy: Emphasis onFrontiers in bioengineering and biotechnology · 2023Review
- Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features.BMC bioinformatics · 2022Article
- Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics.Microorganisms · 2022Article
- Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.Molecular diversity · 2022Article
- SMRI: A New Method for siRNA Design for COVID-19 Therapy.Journal of computer science and technology · 2022Article
- MMV-db: vaccinomics and RNA-based therapeutics database for infectious hemorrhagic fever-causing mammarenaviruses.Database : the journal of biological databases and curation · 2021Article
- Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm.Computers in biology and medicine · 2021Article
- Article
- Antiviral efficacy of short-hairpin RNAs and artificial microRNAs targeting foot-and-mouth disease virus.PeerJ · 2021Article
- CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2.Frontiers in microbiology · 2020Article
- NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus.Database : the journal of biological databases and curation · 2020Article
- Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma.PloS one · 2020Article
- siRNA Design to Silence the 3'UTR Region of Zika Virus.BioMed research international · 2020Article
- Computational Identification of piRNAs Using Features Based on RNA Sequence, Structure, Thermodynamic and Physicochemical Properties.Current genomics · 2019Article
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3 authors.
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Abstract
backgroundSelection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed "VIRsiRNApred" a support vector machine (SVM) based method for predicting the efficacy of viral siRNA.
methodsIn the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T1380) and validation (V345) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development.
resultsDuring 10-fold cross validation on T1380 using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V345 independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree.
conclusionVIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/.
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