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下一代计算瘤模型的挑战和机遇

Lance L Munn¹, Rakesh K Jain¹

¹Edwin L Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.

|July 23, 2025

概括

人工智能 (AI) 正在推进瘤建模,克服了对传统数学模型所需的广泛生物数据和多学科专业知识的需求.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

数学模型对于理解癌细胞和微环境相互作用至关重要.
开发这些模型需要大量的多学科专业知识和大量的生物数据.
传统的建模方法面临复杂性和数据要求的局限性.

研究的目的:

探索人工智能 (AI) 在推进瘤建模中的作用.
突出AI克服当前数学癌症模型局限性的潜力.
引入下一代由人工智能驱动的瘤建模.

主要方法:

审查当前的AI应用在癌症研究.
分析AI处理大型生物数据集的能力.
评估AI整合各种生物信息的潜力.

主要成果:

人工智能显著减少了在模型开发中需要广泛的生物数据的需求.
人工智能有助于将多学科专业知识整合到计算模型中.
人工智能可以创建更复杂和更具预测性的瘤模型.

结论:

人工智能代表了开发先进瘤模型的范式转变.
人工智能驱动的方法有望加速癌症研究和治疗开发.
瘤建模的未来在于人工智能和生物数据的整合.

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