<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Bie Y</submitter><funding>Chinese Academy of Sciences</funding><funding>Ministry of Science and Technology of the People&amp;apos;s Republic of China</funding><funding>National Natural Science Foundation of China</funding><funding>National Key Research and Development Program of China</funding><pagination>645-654</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11401469</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>39(4)</volume><pubmed_abstract>The increasing emergence and re-emergence of RNA virus outbreaks underlines the urgent need to develop effective antivirals. RNA interference (RNAi) is a sequence-specific gene silencing mechanism that is triggered by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), which exhibits significant promise for antiviral therapy. AGO2-dependent shRNA (agshRNA) generates a single-stranded guide RNA and presents significant advantages over traditional siRNA and shRNA. In this study, we applied a logistic regression algorithm to a previously published chemically siRNA efficacy dataset and built a machine learning-based model with high predictive power. Using this model, we designed siRNA sequences targeting diverse RNA viruses, including human enterovirus A71 (EV71), Zika virus (ZIKV)</pubmed_abstract><journal>Virologica Sinica</journal><pubmed_title>Design of antiviral AGO2-dependent short hairpin RNAs.</pubmed_title><pmcid>PMC11401469</pmcid><funding_grant_id>2023YFC2305900</funding_grant_id><funding_grant_id>82172269</funding_grant_id><funding_grant_id>U21A20423</funding_grant_id><funding_grant_id>82222028</funding_grant_id><pubmed_authors>Qiu Y</pubmed_authors><pubmed_authors>Bie Y</pubmed_authors><pubmed_authors>Zhang J</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Zhang Y</pubmed_authors><pubmed_authors>Huang M</pubmed_authors><pubmed_authors>Zhang L</pubmed_authors><pubmed_authors>Zhou X</pubmed_authors></additional><is_claimable>false</is_claimable><name>Design of antiviral AGO2-dependent short hairpin RNAs.</name><description>The increasing emergence and re-emergence of RNA virus outbreaks underlines the urgent need to develop effective antivirals. RNA interference (RNAi) is a sequence-specific gene silencing mechanism that is triggered by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs), which exhibits significant promise for antiviral therapy. AGO2-dependent shRNA (agshRNA) generates a single-stranded guide RNA and presents significant advantages over traditional siRNA and shRNA. In this study, we applied a logistic regression algorithm to a previously published chemically siRNA efficacy dataset and built a machine learning-based model with high predictive power. Using this model, we designed siRNA sequences targeting diverse RNA viruses, including human enterovirus A71 (EV71), Zika virus (ZIKV)</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2026-07-15T13:10:24.383Z</modification><creation>2025-04-06T12:28:51.534Z</creation></dates><accession>S-EPMC11401469</accession><cross_references><pubmed>38734183</pubmed><doi>10.1016/j.virs.2024.05.001</doi></cross_references></HashMap>