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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.10456 |
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| _version_ | 1866910306289057792 |
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| author | Li, Fang Nian, Yi Sun, Zenan Tao, Cui |
| author_facet | Li, Fang Nian, Yi Sun, Zenan Tao, Cui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_10456 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions Li, Fang Nian, Yi Sun, Zenan Tao, Cui Machine Learning Artificial Intelligence 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. |
| title | Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2306.10456 |