• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

更正:启用启发式的主动机器学习:以预测黑腹果蝇的关键发育阶段和免疫反应基因为例的研究。

Correction: Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

作者信息

Aromolaran Olufemi Tony, Isewon Itunuoluwa, Adedeji Eunice, Oswald Marcus, Adebiyi Ezekiel, Koenig Rainer, Oyelade Jelili

出版信息

PLoS One. 2024 Jun 18;19(6):e0305979. doi: 10.1371/journal.pone.0305979. eCollection 2024.

DOI:10.1371/journal.pone.0305979
PMID:38889170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11185443/
Abstract

[This corrects the article DOI: 10.1371/journal.pone.0288023.].

摘要

[本文更正了文章的数字对象标识符:10.1371/journal.pone.0288023。]

相似文献

1
Correction: Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.更正:启用启发式的主动机器学习:以预测黑腹果蝇的关键发育阶段和免疫反应基因为例的研究。
PLoS One. 2024 Jun 18;19(6):e0305979. doi: 10.1371/journal.pone.0305979. eCollection 2024.
2
Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.启发式支持的主动机器学习:以预测黑腹果蝇必需发育阶段和免疫反应基因为例的研究。
PLoS One. 2023 Aug 9;18(8):e0288023. doi: 10.1371/journal.pone.0288023. eCollection 2023.
3
Correction: Genome-Wide Association Study on Male Genital Shape and Size in Drosophila melanogaster.更正:黑腹果蝇雄性生殖器形状和大小的全基因组关联研究。
PLoS One. 2018 Oct 2;13(10):e0205301. doi: 10.1371/journal.pone.0205301. eCollection 2018.
4
Correction: independently controlled wing stroke patterns in the fruit fly Drosophila melanogaster.更正:果蝇黑腹果蝇中独立控制的翅膀摆动模式。
PLoS One. 2015 Apr 13;10(4):e0124475. doi: 10.1371/journal.pone.0124475. eCollection 2015.
5
Correction: Back to the light, coevolution between vision and olfaction in the "Dark-flies" (Drosophila melanogaster).更正:回归光明,“暗蝇”(黑腹果蝇)视觉与嗅觉的协同进化。
PLoS One. 2020 Nov 30;15(11):e0243035. doi: 10.1371/journal.pone.0243035. eCollection 2020.
6
Correction: Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.更正:使用机器学习方法预测生物防治剂的直接和间接非目标影响。
PLoS One. 2021 Sep 29;16(9):e0258080. doi: 10.1371/journal.pone.0258080. eCollection 2021.
7
Correction: Diverse radiofrequency sensitivity and radiofrequency effects of mobile or cordless phone near fields exposure in Drosophila melanogaster.更正:黑腹果蝇中移动电话或无绳电话近场暴露的不同射频敏感性和射频效应。
PLoS One. 2019 Nov 12;14(11):e0225304. doi: 10.1371/journal.pone.0225304. eCollection 2019.
8
Correction: Efficient multiplexed genome engineering with a polycistronic tRNA and CRISPR guide-RNA reveals an important role of detonator in reproduction of Drosophila melanogaster.更正:利用多顺反子tRNA和CRISPR引导RNA进行高效多重基因组工程揭示了雷管在黑腹果蝇繁殖中的重要作用。
PLoS One. 2021 Apr 8;16(4):e0250188. doi: 10.1371/journal.pone.0250188. eCollection 2021.
9
Correction: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.更正:使用内在基因组特征的机器学习对新型病原体进行快速分类:COVID-19案例研究
PLoS One. 2021 Jan 27;16(1):e0246465. doi: 10.1371/journal.pone.0246465. eCollection 2021.
10
Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.更正:使用机器学习和知识工程在电子健康记录中检测罕见疾病:急性肝卟啉病案例研究。
PLoS One. 2020 Aug 20;15(8):e0238277. doi: 10.1371/journal.pone.0238277. eCollection 2020.

本文引用的文献

1
Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.启发式支持的主动机器学习:以预测黑腹果蝇必需发育阶段和免疫反应基因为例的研究。
PLoS One. 2023 Aug 9;18(8):e0288023. doi: 10.1371/journal.pone.0288023. eCollection 2023.