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.
Cite
@article{arxiv.2306.10456,
title = {Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions},
author = {Fang Li and Yi Nian and Zenan Sun and Cui Tao},
journal= {arXiv preprint arXiv:2306.10456},
year = {2024}
}
Comments
Accepted by 2023 IMIA Yearbook of Medical Informatics