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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.04103 |
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| _version_ | 1866909888362315776 |
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| author | Cattaneo, Alberto Bonner, Stephen Martynec, Thomas Morrissey, Edward Luschi, Carlo Barrett, Ian P Justus, Daniel |
| author_facet | Cattaneo, Alberto Bonner, Stephen Martynec, Thomas Morrissey, Edward Luschi, Carlo Barrett, Ian P Justus, Daniel |
| contents | Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models have been proposed over the years. However, little is known about the properties that render a dataset, and associated modelling choices, useful for a given task. Moreover, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. In this work, we conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world tasks. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models Cattaneo, Alberto Bonner, Stephen Martynec, Thomas Morrissey, Edward Luschi, Carlo Barrett, Ian P Justus, Daniel Machine Learning Artificial Intelligence Quantitative Methods Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models have been proposed over the years. However, little is known about the properties that render a dataset, and associated modelling choices, useful for a given task. Moreover, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. In this work, we conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world tasks. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications. |
| title | The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2409.04103 |