EAGER: Similarity Measures Based on Refinement Operators and Metric Embedding Applied to the Analysis of Immune Repertoires

相似性的概念在现代机器学习和人工智能（AI）中起着关键作用，因为它是算法对对象进行分类、形成概念和进行概括的组织原则。虽然相似性评估已被广泛研究，但在感兴趣的数据被结构化的领域中评估相似性的重要特殊情况尚未得到足够的重视。然而，这些结构化表示在许多领域中起着关键作用，例如生物医学，其中感兴趣的数据自然适合于结构化表示。本研究的目的是通过建立一个新的通用的相似性评估框架来填补结构相似性知识的差距。为了实现这一框架的创建，PI将专注于免疫细胞群体的特定生物医学应用及其在发育和应对疾病过程中的动态。通过专注于这个特定的领域，所进行的研究将在现实世界中评估新方法，同时对免疫动力学的理解做出重大贡献。在这个研究项目中将开发的关键概念是细化算子和度量嵌入。所提出的工作的关键见解是，细化运营商可以用来定义相似性的措施，并抽象远离底层的表示形式主义。这将导致一个新的框架相似性评估，适用于广泛的代表形式主义。此外，我们建议使用度量嵌入技术，提供计算效率的数值近似所产生的相似性措施。适用于一系列结构化表示的通用和易处理的相似性度量的定义将对结构化机器学习和人工智能做出重大贡献。该研究小组将使用从高通量测序实验中收集的数据，并通过使用它们来分析如何通过克隆型（具有相同祖细胞的细胞集）描述和比较免疫细胞群体的库来评估所提出的相似性度量的一般性和性能。将相似性度量应用于这个问题的结果将帮助我们开始构建一个全面的观点，即克隆型和整个库信息对我们理解免疫反应动态的影响。

The notion of similarity plays a key role in modern machine learning and artificial intelligence (AI) in general, since it serves as an organizing principle by which algorithms classify objects, form concepts, and make generalizations. While similarity assessment has been widely studied, the important special case of assessing similarity in domains where the data of interest is structured has not received sufficient attention. These structured representations, however, play a key role in many domains, such as biomedicine, where data of interest naturally lends itself to structured representations. The research performed in this project aims at filling the gap in structural similarity knowledge by creating a novel generalized framework for similarity assessment. To achieve the creation of this framework the PIs will focus on the specific biomedical application of immune cell populations and their dynamics during development and in response to disease. By focusing on this specific domain, the performed research will evaluate the new approach in a real-world setting, while leading to significant contributions to the understanding of immune dynamics.The key concepts that will be developed in this research project are refinement operators and metric embedding. The key insight of the proposed work is that refinement operators can be used to define similarity measures, and to abstract away from the underlying representation formalism. This will lead to a new framework for similarity assessment that is applicable to a broad range of representation formalisms. Moreover, we propose to use metric embedding techniques to provide computationally efficient numerical approximations to the resulting similarity measures. The definition of general and tractable similarity measures, applicable to a range of structured representations, will be a significant contribution to structured machine learning and AI. The research team will use data collected from high throughput sequencing experiments, and evaluate the generality and performance of the proposed similarity measures by using them to analyze how repertoires of immune cell populations can be described and compared by their clonotypes (sets of cells with the same progenitor cell). The results from applying similarity measures to this problem will help us start to construct a comprehensive view of the impact of clonotype and whole repertoire information on our understanding of the dynamics of immune responses in general.