ArticleBMC genomics2010
Predicting siRNA potency with random forests and support vector machines.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- SiaRNA: a siamese neural network with bidirectional cross-attention for pairwise siRNA-mRNA efficacy prediction.Frontiers in bioinformatics · 2026Article
- siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.Briefings in bioinformatics · 2024Article
- Asymmetric trichotomous partitioning overcomes dataset limitations in building machine learning models for predicting siRNA efficacy.Molecular therapy. Nucleic acids · 2023Article
- Transgene-free genome editing and RNAi ectopic application in fruit trees: Potential and limitations.Frontiers in plant science · 2022Review
- Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level.Scientific reports · 2017Article
- Investigation and identification of protein carbonylation sites based on position-specific amino acid composition and physicochemical features.BMC bioinformatics · 2017Article
- Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits.Frontiers in plant science · 2016Article
- Computational design of artificial RNA molecules for gene regulation.Methods in molecular biology (Clifton, N.J.) · 2015Article
- Incorporating amino acids composition and functional domains for identifying bacterial toxin proteins.BioMed research international · 2014Article
- Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles.Journal of computer-aided molecular design · 2014Article
- Article
- Development of Therapeutic-Grade Small Interfering RNAs by Chemical Engineering.Frontiers in genetics · 2012Article
- Incorporating evolutionary information and functional domains for identifying RNA splicing factors in humans.PloS one · 2011Article
- 2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research.BMC genomics · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundShort interfering RNAs (siRNAs) can be used to knockdown gene expression in functional genomics. For a target gene of interest, many siRNA molecules may be designed, whereas their efficiency of expression inhibition often varies.
resultsTo facilitate gene functional studies, we have developed a new machine learning method to predict siRNA potency based on random forests and support vector machines. Since there were many potential sequence features, random forests were used to select the most relevant features affecting gene expression inhibition. Support vector machine classifiers were then constructed using the selected sequence features for predicting siRNA potency. Interestingly, gene expression inhibition is significantly affected by nucleotide dimer and trimer compositions of siRNA sequence.
conclusionsThe findings in this study should help design potent siRNAs for functional genomics, and might also provide further insights into the molecular mechanism of RNA interference.
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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.