假设驱动的人工智能在瘤学中的兴起
Zilin Xianyu1, Cristina Correia1, Choong Yong Ung1
1Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA.
Cancers
|February 24, 2024
概括
假设驱动的人工智能 (AI) 提供了一种新的方法,可以使用大omics数据来理解癌症的复杂性. 这种方法整合了科学假设,使得癌症研究更加可解释和以发现为导向.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 在瘤学瘤学.
背景情况:
- 癌症涉及复杂的细胞放松监管超出了遗传学,需要先进的计算方法和高维数据分析.
- 传统的人工智能 (AI) 模型往往缺乏可解释性,并且无法纳入研究人员生成的科学假设,从而限制了它们在癌症发现中的实用性.
- 现有的AI方法很难从大型omics数据集中揭示癌症的复杂病因.
研究的目的:
- 引入假设驱动的人工智能作为癌症研究的创新方法.
- 为了证明假设驱动的AI在解释复杂的癌症数据方面与传统AI有何不同.
- 突出将领域知识和科学假设整合到用于新型癌症发现的AI算法设计中的潜力.
主要方法:
- 在瘤学中对基于假设的AI应用进行审查.
- 人工智能在瘤分类,患者分层,基因发现,药物反应预测和瘤空间组织方面的示例.
- 讨论将领域知识和科学假设纳入AI算法设计.
主要成果:
- 由假设驱动的AI使得从大omics数据中发现癌症的复杂病因.
- 这种方法促进了新的癌症发现,这些发现可能会被传统的人工智能方法遗漏.
- 应用涵盖了各种瘤领域,展示了科学知识与AI的整合.
结论:
- 假设驱动的人工智能对发现癌症病因学的新机制和功能洞察力具有重大前景.
- 它为改善AI在癌症研究中的解释性和发现潜力提供了一个新的范式.
- 需要进一步的研究来解决知识整合,偏见减少和AI算法设计中的可解释性方面的挑战.
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