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文献精选 > 近年高分"AI药物发现"研究（IF≥30，近5年）

Artificial Intelligence in Cancer Research and Precision Medicine. ​

文献信息 ​

DOI10.1158/2159-8290.CD-21-0090
PMID33811123
期刊Cancer discovery
影响因子33.3
JCR 分区Q1
发表年份2021
被引次数283
关键词人工智能, 癌症研究, 精准医学, 药物发现, 治疗结果预测
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
ISSN2159-8274
页码900-915
期号11(4)
作者Bhavneet Bhinder, Coryandar Gilvary, Neel S Madhukar, Olivier Elemento

一句话小结 ​

人工智能（AI）正在迅速改变癌症研究和个性化治疗，利用高维数据和深度学习技术推动癌症检测、分类、药物发现及患者结果预测的应用。本文回顾了AI在肿瘤学中的进展，指出其潜在影响和局限性，并为未来在临床中有效采纳AI提供指导。

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人工智能 · 癌症研究 · 精准医学 · 药物发现 · 治疗结果预测

摘要 ​

人工智能（AI）正迅速重塑癌症研究和个性化临床护理。高维数据集的可用性，加上高性能计算的进步以及创新的深度学习架构，导致了AI在肿瘤学研究各个方面应用的激增。这些应用范围涵盖了癌症的检测与分类、肿瘤及其微环境的分子特征分析、药物发现与再利用，以及预测患者的治疗结果。随着这些进展开始渗透到临床中，我们预见到癌症护理的范式将受到AI的强烈推动而发生转变。重要性：AI有潜力显著影响肿瘤学的几乎所有方面——从增强诊断到个性化治疗，再到发现新型抗癌药物。在此，我们回顾了AI在肿瘤学应用中的巨大进展，强调了其局限性和陷阱，并为在癌症临床中采纳AI绘制了一条路径。

英文摘要 ​

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

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主要研究问题 ​

  1. 在癌症研究中，人工智能如何具体提高早期诊断的准确性？
  2. 目前有哪些成功的案例展示了人工智能在药物发现过程中的应用？
  3. 人工智能在个性化治疗中面临哪些伦理和隐私挑战？
  4. 如何评估人工智能模型在肿瘤微环境分子特征分析中的有效性？
  5. 随着人工智能技术的发展，未来的癌症临床护理可能会有哪些新的变革？

核心洞察 ​

研究背景和目的 ​

随着高维数据集的可用性、计算能力的提升以及深度学习架构的创新，人工智能（AI）正在迅速改变癌症研究和个性化临床护理。本文旨在回顾AI在肿瘤学中的最新应用进展，探讨其在癌症检测、分类、分子特征分析、药物发现及预测治疗结果等方面的潜力，同时分析AI在临床应用中的局限性和挑战。

主要方法/材料/实验设计 ​

本研究主要通过文献回顾和案例分析的方式，评估AI在癌症研究中的应用。以下是AI应用于肿瘤学的技术路线示意图：

Mermaid diagram

关键结果和发现 ​

  • 检测与分类：AI技术在癌症的早期检测和准确分类方面表现出色，能够提高诊断的敏感性和特异性。
  • 分子特征分析：AI能够深入分析肿瘤及其微环境的分子特征，帮助识别潜在的生物标志物。
  • 药物发现：AI在药物发现和再利用过程中显著加速了候选药物的筛选和优化过程。
  • 治疗结果预测：AI模型能够基于患者的个体特征和治疗历史，预测治疗效果，帮助制定个性化治疗方案。

主要结论/意义/创新性 ​

AI在肿瘤学的应用展现出巨大的潜力，能够从多个方面提升癌症的诊断、治疗和药物研发效率。其创新性体现在：

  • 提高了癌症早期检测的准确性。
  • 加速了新药的发现过程。
  • 通过个性化治疗提升患者的生存率和生活质量。

研究局限性和未来方向 ​

  • 局限性：AI模型的有效性依赖于高质量的数据集，当前数据的多样性和代表性不足，可能影响模型的普适性。
  • 未来方向：
    • 加强数据共享与标准化，以提高AI模型的训练效果。
    • 深入研究AI在临床实践中的可接受性和伦理问题。
    • 发展可解释的AI技术，以增强临床医生对AI决策的信任。
部分内容
研究背景和目的探讨AI在癌症研究中的应用及其潜力
主要方法/材料/实验设计文献回顾与案例分析，技术路线图展示AI应用
关键结果和发现提高检测准确性、加速药物发现、个性化治疗效果预测
主要结论/意义/创新性AI显著提升癌症诊断和治疗效率，展现出巨大的应用潜力
研究局限性和未来方向数据质量不足、未来需关注数据共享、伦理问题及可解释性研究

参考文献 ​

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引用本文的文献 ​

  1. Preclinical models as patients' avatars for precision medicine in colorectal cancer: past and future challenges. - Erika Durinikova;Kristi Buzo;Sabrina Arena - Journal of experimental & clinical cancer research : CR (2021)
  2. Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy. - Shengli Shao;Lu Liu;Yufeng Zhao;Lei Mu;Qiyi Lu;Jichao Qin - Journal of personalized medicine (2021)
  3. Harnessing multimodal data integration to advance precision oncology. - Kevin M Boehm;Pegah Khosravi;Rami Vanguri;Jianjiong Gao;Sohrab P Shah - Nature reviews. Cancer (2022)
  4. Can Systems Biology Advance Clinical Precision Oncology? - Andrea Rocca;Boris N Kholodenko - Cancers (2021)
  5. Understanding Drug Sensitivity and Tackling Resistance in Cancer. - Jeffrey W Tyner;Franziska Haderk;Anbarasu Kumaraswamy;Linda B Baughn;Brian Van Ness;Song Liu;Himangi Marathe;Joshi J Alumkal;Trever G Bivona;Keith Syson Chan;Brian J Druker;Alan D Hutson;Peter S Nelson;Charles L Sawyers;Christopher D Willey - Cancer research (2022)
  6. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. - Octav Ginghina;Ariana Hudita;Marius Zamfir;Andrada Spanu;Mara Mardare;Irina Bondoc;Laura Buburuzan;Sergiu Emil Georgescu;Marieta Costache;Carolina Negrei;Cornelia Nitipir;Bianca Galateanu - Frontiers in oncology (2022)
  7. Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity. - Irene Dankwa-Mullan;Dilhan Weeraratne - Cancer discovery (2022)
  8. Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy. - Ahmed Halima;Winston Vuong;Timothy A Chan - The Journal of clinical investigation (2022)
  9. Targeting Cell Cycle Progression in HER2+ Breast Cancer: An Emerging Treatment Opportunity. - Nischal Koirala;Nandini Dey;Jennifer Aske;Pradip De - International journal of molecular sciences (2022)
  10. A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation. - Sayed Mehedi Azim;Alok Sharma;Iman Noshadi;Swakkhar Shatabda;Iman Dehzangi - Scientific reports (2022)

... (273 更多 篇文献)

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