Graph Representation Learning for Biomedical Entities

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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.