{"title":"Correction to: \"Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency\".","authors":"","doi":"10.1093/bioinformatics/btad562","DOIUrl":null,"url":null,"abstract":"This is a correction to “Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency”, Bioinformatics, Volume 39, Issue 7, July 2023, https://doi.org/10.1093/bioin formatics/btad412. The retraction notice text has been updated, because we have subsequently discovered that the authors did not receive the journal’s communications to them asking them to address the flaws. This correction does not change the outcome or decision to retract.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500088/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad562","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
This is a correction to “Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency”, Bioinformatics, Volume 39, Issue 7, July 2023, https://doi.org/10.1093/bioin formatics/btad412. The retraction notice text has been updated, because we have subsequently discovered that the authors did not receive the journal’s communications to them asking them to address the flaws. This correction does not change the outcome or decision to retract.
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The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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