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)中发挥着关键作用，因为它是算法分类对象、形成概念和进行归纳的组织原则。虽然相似性评价已经得到了广泛的研究，但在结构化感兴趣数据的领域中进行相似性评价这一重要的特殊情况还没有得到足够的重视。然而，这些结构化表示在许多领域中扮演着关键角色，例如生物医学，其中感兴趣的数据自然地将其自身借给结构化表示。本项目的研究旨在通过建立一种新的通用相似性评估框架来填补结构相似性知识的空白。为了实现这一框架的建立，私人投资机构将侧重于免疫细胞群体的具体生物医学应用及其在发育和应对疾病过程中的动态。通过专注于这一特定领域，所进行的研究将在现实世界中对新方法进行评估，同时对理解免疫动力学做出重大贡献。本研究项目中将开发的关键概念是精化算子和度量嵌入。提出的工作的关键见解是，精化算子可用于定义相似性度量，并从底层的表示形式主义中抽象出来。这将导致适用于广泛的表示形式主义的相似性评估的新框架。此外，我们建议使用度量嵌入技术来为所得到的相似性度量提供计算上有效的数值近似。适用于一系列结构化表示的一般和易处理的相似性度量的定义将是对结构化机器学习和人工智能的重大贡献。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类型(具有相同祖细胞的细胞组)来描述和比较免疫细胞群体的谱系，从而评估拟议的相似性衡量标准的一般性和性能。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视图，了解克隆类型和整个谱系信息对我们总体上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.