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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Li, Zhijing Lian, Yuchen Ma, Xiaoyong Zhang, Xiangrong Li, Chen |
| Abstract | Background Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge. Results The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining. Conclusion The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts. |
| Related Links | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-3540-8.pdf |
| Ending Page | 18 |
| Page Count | 18 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712105 |
| DOI | 10.1186/s12859-020-3540-8 |
| Journal | BMC Bioinformatics |
| Issue Number | 1 |
| Volume Number | 21 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2020-05-24 |
| Access Restriction | Open |
| Subject Keyword | Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Bio-text-mining Biological semantic relation Knowledge base Attention mechanism Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 3.6/2023 |
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