Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Fang Li; Yi Nian; Zenan Sun; Cui Tao

doi:10.1055/s-0043-1768735

Yearbook of Medical Informatics, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Yearb Med Inform 2023; 32(01): 215-224
DOI: 10.1055/s-0043-1768735

Section 9: Knowledge Representation and Management

Survey

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Autoren

Fang Li

McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
Yi Nian

McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
Zenan Sun

McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
Cui Tao

McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA

Abstract

Summary

Objectives: Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.

Methods: We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality.

Results: A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions.

Conclusions: GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.

Keywords

Graph representation learning - biomedicine - graph neural network - knowledge graph

Volltext

Referenzen

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