III: Medium: Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis

癌症是世界上最致命的恶性疾病之一。生存分析，预测事件发生的时间（即，在癌症的情况下死亡）对于癌症生物学研究和疾病治疗至关重要。使用肿瘤的分子信息根据疾病阶段或严重程度对患者进行分层对于为每位患者定制治疗至关重要。识别推动预测不良生存结局的分子特征可以为癌症的机制研究提供见解，并导致新的治疗靶点的发现。高通量基因组学技术的最新进展导致了大量的高维组学数据可用，这使得应用机器学习方法来构建预测模型成为可能，这些模型可以预测肿瘤的阶段或严重程度，并确定驱动肿瘤发生和转移的关键分子因素，从而可能导致新的治疗靶点。虽然深度学习已被应用于癌症生存分析，但它在每个队列中具有相对较少数量的具有高维特征的样本的生物数据上效果不太好（即，著名的大p，小n问题）。癌症基因组图谱（TCGA）就是这样一个例子，它表征了跨越许多癌症类型的多水平高维临床和分子谱。为了使机器学习有效地处理少量训练数据，例如TCGA中的每种癌症类型，我们提出了一套先进的机器学习方法来解决癌症生存分析中众所周知的大p，小n问题。本研究开发了一种新的知识引导的元学习框架，创新性地将生物学知识与元学习相结合，用于多组学生存分析。该框架解决了许多领域的场景所带来的一系列技术挑战（例如，癌症），但每个域中的样本数量较少，并且利用少量学习的原理通过训练一组不同的癌症来实现对新癌症样本的快速适应过程。所提出的知识引导的嵌入学习框架制定了一个新的基本结构，使生物学知识整合到一个潜在的患者表示的建设，它利用丰富的信息存在于人类策划的知识库，如基因本体和监管网络，可以在不同的癌症共享。所提出的生存分析方法的元学习能够预测来自未知癌症的患者的生存概率。为了进一步增强生存预测的元学习，开发了一种称为具有元知识的上下文感知学习的元学习框架，其明确地将策划的领域知识纳入元学习框架中，并且知道预测上下文，包括输入患者样本的预测难度和异质性。所提出的知识引导元学习范式在适应新任务或领域方面比传统迁移学习更强大。该项目将导致一种新的，先进的方法来分析各种癌症数据集，每种癌症都具有小样本量。 因此，该项目具有极大的潜力，可以推进少量学习的理论和实践，并具有强烈的社会影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Cancers are among the world's most deadly malignant diseases. Survival analysis, which predicts the time to an event (i.e., death in the case of cancer), is of crucial importance for cancer biology research and disease treatment. Stratifying patients according to disease stage or severity using molecular information from the tumor is critical for tailoring treatments for each patient. Identifying molecular features that drive the prediction of poor survival outcomes could provide insights into mechanistic studies of cancer and lead to discoveries of new therapeutic targets. Recent advances in high-throughput genomics technologies have led to a massive amount of high-dimensional omics data being available which makes it possible to apply machine learning approaches to build predictive models that predict tumor stage or severity as well as identifying the key molecular factors driving tumorigenesis and metastasis which could lead to new therapeutic targets. While deep learning has been applied to cancer survival analysis, it does not work very well on biological data with a relatively small number of samples with high-dimensional features in each cohort (i.e., the well-known big p, small n problem). The Cancer Genome Atlas (TCGA) is such an example, which characterizes multi-level high-dimensional clinical and molecular profiles spanning many cancer types. To make machine learning effectively work on a small amount of training data such as each cancer type in TCGA, we propose a set of advanced machine learning approaches to tackle the well-known big p, small n problem for cancer survival analysis. This project develops a new knowledge-guided meta-learning framework which innovatively integrates biological knowledge with meta-learning for multi-omics survival analysis. This framework tackles a series of technical challenges posed by the scenario of many domains (e.g., cancer) but small numbers of samples in each domain and utilizes the principle of few-shot learning to implement a fast adaptation process to new cancer samples by training of a set of different cancers. The proposed knowledge-guided embedding-learning framework formulates a new fundamental structure that enables the integration of biological knowledge into the construction of a latent patient representation, which exploits the rich information present in human-curated knowledge bases such as gene ontology and regulatory networks that can be shared across different cancers. The proposed meta-learning for survival analysis approach enables the prediction of survival probability for patients from unseen cancers. To further enhance meta-learning for survival predictions, a meta-learning framework called Context-Aware Learning with Meta-Knowledge is developed, which explicitly incorporates curated domain knowledge into the meta-learning framework, and is aware of the prediction context, including the prediction difficulty and heterogeneity of the input patient samples. The proposed paradigm of knowledge-guided meta-learning is significantly more powerful than traditional transfer learning in adapting to new tasks or domains. The project will lead to a novel, advanced approach to analyzing a variety of cancer datasets with each cancer having a small sample size. Thus, this project has significant potential to advance the theory and practice of few-shot learning, with strong social implications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.