• 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

相似文献

1
Corrigendum: Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response in lung adenocarcinoma.勘误:利用单细胞和多组学见解:基于STING通路的肺腺癌免疫治疗反应预测特征。
Front Immunol. 2025 Apr 30;16:1613095. doi: 10.3389/fimmu.2025.1613095. eCollection 2025.
2
Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response in lung adenocarcinoma.利用单细胞和多组学见解:基于STING通路的肺腺癌免疫治疗反应预测特征
Front Immunol. 2025 Apr 16;16:1575084. doi: 10.3389/fimmu.2025.1575084. eCollection 2025.
3
Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.整合多组学分析和机器学习以细化肺腺癌的分子亚型、预后和免疫治疗。
Funct Integr Genomics. 2024 Jun 27;24(4):118. doi: 10.1007/s10142-024-01388-x.
4
Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.整合多组学和机器学习揭示了肺腺癌预后和治疗反应的吉非替尼耐药特征。
IUBMB Life. 2025 Jan;77(1):e2930. doi: 10.1002/iub.2930. Epub 2024 Nov 29.
5
Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations.基于多种机器学习组合的肺腺癌中GPCR基因特征的多组学鉴定
J Cancer. 2024 Jan 1;15(3):776-795. doi: 10.7150/jca.90990. eCollection 2024.
6
Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies.解读B细胞在肺腺癌预后及定制化治疗策略中的关键作用:一种针对预测、预防和个性化治疗策略的多组学与机器学习方法
EPMA J. 2024 Dec 17;16(1):127-163. doi: 10.1007/s13167-024-00390-4. eCollection 2025 Mar.
7
Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.基于单细胞和 bulk-RNA 测序的机器学习和联合分析,确定了一个 DC 基因特征,用于预测肺腺癌患者的预后和免疫治疗反应。
J Cancer Res Clin Oncol. 2023 Nov;149(15):13553-13574. doi: 10.1007/s00432-023-05151-w. Epub 2023 Jul 28.
8
Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.通过人工智能驱动的干性相关基因特征解读肺腺癌的预后和免疫治疗反应。
J Cell Mol Med. 2024 Jul;28(14):e18564. doi: 10.1111/jcmm.18564.
9
Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.多组学和单细胞分析揭示了基于机器学习的嘧啶代谢相关特征在肺腺癌患者预后中的作用。
Int J Med Sci. 2025 Feb 18;22(6):1375-1392. doi: 10.7150/ijms.107694. eCollection 2025.
10
Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.肺腺癌分子亚型的多组学特征分析及机器学习以指导精准化疗和免疫治疗
Front Immunol. 2024 Nov 28;15:1497300. doi: 10.3389/fimmu.2024.1497300. eCollection 2024.

勘误:利用单细胞和多组学见解:基于STING通路的肺腺癌免疫治疗反应预测特征。

Corrigendum: Harnessing single-cell and multi-omics insights: STING pathway-based predictive signature for immunotherapy response in lung adenocarcinoma.

作者信息

Ding Yang, Wang Dingli, Yan Dali, Fan Jun, Ding Zongli, Xue Lei

机构信息

Department of Pathology, Nanjing Drum Tower Hospital Group Suqian Hospital, Suqian, China.

Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

出版信息

Front Immunol. 2025 Apr 30;16:1613095. doi: 10.3389/fimmu.2025.1613095. eCollection 2025.

DOI:10.3389/fimmu.2025.1613095
PMID:40370448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12075954/
Abstract

[This corrects the article DOI: 10.3389/fimmu.2025.1575084.].

摘要

[本文更正了文章的数字对象标识符:10.3389/fimmu.2025.1575084。]