Graph Representation Learning for Biomedical Entities

近年来，图表示学习迅速成为机器学习研究的主要推动力之一。图表示的强大之处在于它能够自然地表示实体及其相互作用。将数据的图结构表示中包含的归纳偏差纳入机器学习模型，已经在许多研究领域取得了重大进展。生物医学研究特别受益于图表示学习方法，因为网络可以用来表示大量的生物实体及其关联。这些应用和方法旨在为不同的任务找到适当的生物网络表示。然而，如何有效地结合来自不同网络的信息，目前还不清楚。药物开发、疾病进展分析、治疗反应设计和其他一些任务需要考虑几个生物网络内部和之间的复杂关系。传统的网络表示不足以处理对象和关系的异质性，因此需要一个更丰富的框架来处理不同尺度的对象和关系。该提案旨在开发新的图表示学习方法，该方法可以集成和解释节点和边具有不同含义的复杂，多路和多层网络。

Graph representation learning has rapidly become one of the main drivers in machine learning research over recent years. The power of graph representation lies in its ability to naturally represent entities and their interactions. Incorporating in machine learning models the inductive bias contained in the graph-structured representation of the data, has led to significant advances in many areas of research. Biomedical research has particularly benefited of graph representation learning methodologies, since networks can be used to represent a multitude of biological entities and their associations. These applications and methodologies aim to find appropriate representations of biological networks for different tasks. However, it is still unclear how to effectively combine information originated from different networks. Drug development, analysis of disease progression, therapeutic response design and several more tasks require considering the complex relationships within and among several biological networks. Traditional network representations are insufficient for dealing with the heterogeneity of objects and relationships, so a richer framework is needed to handle objects and relationships of different scales. This proposal aims to develop new graph representation learning methods that can integrate and explain complex, multiplex, and multilayer networks where nodes and edges have different meanings.