Identifying Protein-Protein Interaction using Tree-Transformers and Heterogeneous Graph Neural Network
For a better understanding of the underlying biological mechanisms, it is crucial to identify the reciprocity between proteins. Often, extracting such interactions between proteins from biomedical articles faces challenges due to the complex sentence structure of the textual information sources. Mos...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133256 |
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| Summary: | For a better understanding of the underlying biological mechanisms, it is crucial to identify the reciprocity between proteins. Often, extracting such interactions between proteins from biomedical articles faces challenges due to the complex sentence structure of the textual information sources. Most of the prominent previous works have applied additional hand-crafted features for the protein-protein interaction task. In this work, we have utilized two tree-structured attention-based neural network models along with a heterogeneous graph approach to perform this task. We suggest that the proposed model preserves the syntactic as well as the semantic information of the text. The experimental results demonstrate that even without using any additional feature extraction techniques, this model achieves significant performance boosts when applied on the five standard benchmark corpora compared to the previous works. |
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| ISSN: | 2334-0754 2334-0762 |