ArticleMolecular therapy. Nucleic acids2025
shRNAI: A deep neural network for the design of highly potent shRNAs.
Article in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Beyond inflammation: siRNA strategies for precision targeting in rheumatological disorders.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- CAG-targeting artificial miRNA with reduced off-target risk for efficient lowering of pathogenic polyglutamine proteins.NAR molecular medicine · 2026Article
- Unleashing lncRNA THOR: roles in cancer progression and clinical outlook.Molecular genetics and genomics : MGG · 2026Review
- Coatomer protein complex I is required for efficient secretion of dengue virus non-structural protein 1.Journal of virology · 2025Article
- BBANsh: a deep learning architecture based on BERT and bilinear attention networks to identify potent shRNA.Briefings in bioinformatics · 2025Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
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Authors and funding
8 authors.
Funding
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Abstract
miRNA-mimicking short hairpin RNAs (shRNAmirs), which depend on the endogenous miRNA biogenesis pathway, have been widely used to investigate gene function and to develop therapeutic strategies due to their stable and robust knockdown of target genes. However, despite the efforts to design potent shRNAmir guide RNAs (gRNAs), relevant biological features beyond the primary sequence have not been fully explored. Here, we present shRNAI, a convolutional neural network model for predicting highly potent shRNAmir gRNAs. Even when trained solely on gRNA sequences, shRNAI outperforms previous algorithms. We further improved the model (shRNAI+) by adding features related to shRNAmir processability and target site context, resulting in superior performance across both public datasets and our own experimental tests. Although shRNAI was initially trained on datasets built with a CNNC motif-free pri-miR-30 backbone, it also displayed improved performance on the CNNC motif. Overall, our study provides a robust framework for designing potent shRNAmir gRNAs, as well as a versatile tool for developing RNAi therapeutics.
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Registered trials
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