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人工智能在优化癌症治疗中的应用:为跨学科进展奠定基础。

AI in optimized cancer treatment: laying the groundwork for interdisciplinary progress.

作者信息

Kalweit Gabriel, Ullrich Evelyn, Boedecker Joschka, Mertelsmann Roland, Kalweit Maria

机构信息

Collaborative Research Institute Intelligent Oncology (CRIION), Freiburg, Baden-Württemberg, 79110, Germany.

Department of Computer Science, University of Freiburg, Freiburg, Baden-Württemberg, 79110, Germany.

出版信息

Oxf Open Immunol. 2025 May 12;6(1):iqaf004. doi: 10.1093/oxfimm/iqaf004. eCollection 2025.

DOI:10.1093/oxfimm/iqaf004
PMID:40417176
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12103913/
Abstract

The molecular complexity of cancer presents significant challenges to traditional therapeutic approaches, necessitating the development of innovative treatment strategies capable of addressing the disease's dynamic nature and resistance mechanisms. Data-driven methodologies, particularly those employing Artificial Intelligence (AI), hold substantial promise by advancing a comprehensive understanding of the intricate molecular and cellular mechanisms underlying cancer and supporting the development of adaptive, patient-specific therapeutic strategies. Initiated through the Mertelsmann Foundation, the Collaborative Research Institute Intelligent Oncology (CRIION) in Freiburg im Breisgau, Germany, aims to drive progress in AI-driven oncology. CRIION fosters global collaboration through initiatives like the Intelligent Oncology Symposium and supports multidisciplinary projects designed to integrate AI innovations into clinical workflows.

摘要

癌症的分子复杂性给传统治疗方法带来了重大挑战,因此需要开发能够应对该疾病动态特性和耐药机制的创新治疗策略。数据驱动的方法,特别是那些采用人工智能(AI)的方法,通过促进对癌症潜在的复杂分子和细胞机制的全面理解,并支持开发适应性强、针对患者的治疗策略,具有巨大的潜力。由默特尔斯曼基金会发起的德国弗赖堡市的智能肿瘤学合作研究所(CRIION)旨在推动人工智能驱动的肿瘤学发展。CRIION通过智能肿瘤学研讨会等倡议促进全球合作,并支持旨在将人工智能创新融入临床工作流程的多学科项目。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0007/12103913/80931f89db3f/iqaf004f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0007/12103913/80931f89db3f/iqaf004f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0007/12103913/80931f89db3f/iqaf004f1.jpg

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Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.利用自动延时显微镜结合深度学习实现集落形成试验自动化。
Front Oncol. 2025 Feb 19;15:1520972. doi: 10.3389/fonc.2025.1520972. eCollection 2025.
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Deficiency of adenosine deaminase 2 skews adaptive immune repertoires toward specific sets of T- and B-cell receptors.
腺苷脱氨酶2缺乏会使适应性免疫库偏向特定的T细胞和B细胞受体组。
J Allergy Clin Immunol. 2025 May;155(5):1664-1674. doi: 10.1016/j.jaci.2025.01.032. Epub 2025 Feb 7.
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Oligoclonality of TRBC1 and TRBC2 in T cell lymphomas as mechanism of primary resistance to TRBC-directed CAR T cell therapies.T细胞淋巴瘤中TRBC1和TRBC2的寡克隆性作为对TRBC定向CAR T细胞疗法原发性耐药的机制
Nat Commun. 2025 Jan 29;16(1):1104. doi: 10.1038/s41467-025-56395-8.
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How to build the virtual cell with artificial intelligence: Priorities and opportunities.如何利用人工智能构建虚拟细胞:优先事项与机遇
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Automated real-world data integration improves cancer outcome prediction.自动化真实世界数据整合可改善癌症预后预测。
Nature. 2024 Dec;636(8043):728-736. doi: 10.1038/s41586-024-08167-5. Epub 2024 Nov 6.
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