Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.
癌症是一种复杂的疾病，其发生不仅涉及基因异常，更牵涉到精密细胞系统的失调，因此需要借助先进的计算方法和高维数据以实现最优解读。尽管传统人工智能模型在许多预测任务中表现出色，但其往往缺乏可解释性，且难以融合研究者为探索癌症而提出的科学假设。本文提出，假设驱动型人工智能作为新兴的人工智能算法类别，为从大规模组学数据中揭示癌症复杂病因提供了一种创新途径。本综述通过引用该技术在肿瘤分类、患者分层、癌症基因发现、药物反应预测及肿瘤空间结构解析等多个肿瘤学领域的应用实例，阐释了假设驱动型人工智能与传统人工智能的本质差异。我们旨在强调将领域知识与科学假设融入新型人工智能算法设计的可行性，并展示假设驱动型人工智能在发现传统方法可能忽略的新颖癌症机制方面的潜力。鉴于该技术仍处于发展初期，如何更好地整合新知识与生物学视角以减少算法设计中的偏差并提升可解释性等开放性问题仍有待探索。总之，假设驱动型人工智能在揭示癌症病因复杂性方面具有巨大潜力，有望为阐释癌症机制与功能提供新见解，并为制定个体化治疗方案开辟新路径。
The Rise of Hypothesis-Driven Artificial Intelligence in Oncology
肿瘤(癌症)患者之家