Protein-Protein Interaction Extraction using Attention-based Tree-Structured Neural Network Models
In order to comprehend underlying biological processes, it is necessary to identify interactions between proteins. It is typically quite difficult to extract a protein-protein interaction (PPI) from text data as text data is complex in nature. Unlike sequential models, tree-structured neural network...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130660 |
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| Summary: | In order to comprehend underlying biological processes, it is necessary to identify interactions between proteins. It is typically quite difficult to extract a protein-protein interaction (PPI) from text data as text data is complex in nature. Unlike sequential models, tree-structured neural network models have the ability to consider syntactic and semantic dependencies between different portions of the text and can provide structural information at the phrase level. This paper investigates tree-structured neural network models for the PPI task and the results show their supremacy over sequential models and their effectiveness for this task. |
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| ISSN: | 2334-0754 2334-0762 |