更正:“撤回:deepcrisstl:深度迁移学习预测CRISPR/Cas9功能和内源性靶向编辑效率”。

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad562

{"title":"更正:“撤回:deepcrisstl:深度迁移学习预测CRISPR/Cas9功能和内源性靶向编辑效率”。","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":null,"pages":null},"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":"{\"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\":null,\"pages\":null},\"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}","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

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Correction to: "Retraction of: DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 functional and endogenous on-target editing efficiency".

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： 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.