Name
Title | ||
|---|---|---|
Extracting Protein-Protein Interactions Affected by Mutations via Auxiliary Task and Domain Pre-trained Model |
Abstract | ||
|---|---|---|
Extracting protein-protein interaction affected by genetic mutation from biomedical literature automatically is an essential step toward the ultimate goal of precision medicine. However, the existing methods fail to be accurate enough to meet the needs in practice. In this paper, considering the significant progress made by the pre-training model in a wide variety of NLP tasks, we use BioBERT to obtain the representation of the text and adopt a multi-task learning strategy to improve the performance. Evaluated on the BioCreative VI PPIm data set, our proposed model achieves a new state-of-the-art performance that surpassed the previous one by 4.86% in F1-score. The source code is available at https://github.com/dlutwy/ppim. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313120 | 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | DocType | ISBN |
PPI extraction,Multi-task Learning,BioBERT | Conference | 978-1-7281-6216-4 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Wang | 1 | 2279 | 211.60 |
| Shaowu Zhang | 2 | 21 | 5.49 |
| Yijia Zhang | 3 | 4 | 5.47 |
| Jian Wang | 4 | 73 | 16.74 |
| Hongfei Lin | 5 | 768 | 122.52 |