ArticleBMC bioinformatics2015
A semi-supervised tensor regression model for siRNA efficacy prediction.
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 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.Briefings in bioinformatics · 2024Article
- Hepatitis A: Viral Structure, Classification, Life Cycle, Clinical Symptoms, Diagnosis Error, and Vaccination.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2023Review
- Therapeutic RNA interference: A novel approach to the treatment of primary hyperoxaluria.British journal of clinical pharmacology · 2022Review
- Article
- FusionHub: A unified web platform for annotation and visualization of gene fusion events in human cancer.PloS one · 2018Article
- Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level.Scientific reports · 2017Article
- Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity.Computational and mathematical methods in medicine · 2017Article
- Direct-acting Antivirals and Host-targeting Agents against the Hepatitis A Virus.Journal of clinical and translational hepatology · 2015Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundShort interfering RNAs (siRNAs) can knockdown target genes and thus have an immense impact on biology and pharmacy research. The key question of which siRNAs have high knockdown ability in siRNA research remains challenging as current known results are still far from expectation.
resultsThis work aims to develop a generic framework to enhance siRNA knockdown efficacy prediction. The key idea is first to enrich siRNA sequences by incorporating them with rules found for designing effective siRNAs and representing them as enriched matrices, then to employ the bilinear tensor regression to predict knockdown efficacy of those matrices. Experiments show that the proposed method achieves better results than existing models in most cases.
conclusionsOur model not only provides a suitable siRNA representation but also can predict siRNA efficacy more accurate and stable than most of state-of-the-art models. Source codes are freely available on the web at: http://www.jaist.ac.jp/\~bao/BiLTR/ .
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Registered trials
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.