Name
Title | ||
|---|---|---|
Hierarchical Recurrent Convolutional Neural Network For Chemical-Protein Relation Extraction From Biomedical Literature |
Abstract | ||
|---|---|---|
Extracting chemical-protein relations between chemicals and proteins plays a key role in various biomedical tasks, such as drug discovery, precision medicine, as well as clinical research. Most popular methods for the chemical-protein interaction (CHEMPROT) task are based on neural networks to avoid the complex hand-crafted features derived from linguistic analyses. However, their performances are usually limited due to long and complicated sentences. Therefore, we propose a novel hierarchical recurrent convolutional neural network (Hierarchical RCNN)-based approach to efficiently learn latent features from short context subsequences. The experimental results on the CHEMPROT corpus show that our method achieves an F-score of 65.56%, and outperforms the state-of-the-art systems. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621159 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
relation extraction, chemical-protein interaction, attention mechanism, Hierarchical RCNN | Convolutional neural network,Computer science,Artificial intelligence,Artificial neural network,Machine learning,Relationship extraction | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Sun | 1 | 7 | 2.15 |
| Zhihao Yang | 2 | 73 | 15.35 |
| Lei Wang | 3 | 56 | 13.90 |
| Yin Zhang | 4 | 36 | 8.78 |
| Hongfei Lin | 5 | 768 | 122.52 |
| Jian Wang | 6 | 73 | 16.74 |
| Liang Yang | 7 | 120 | 42.20 |
| Kan Xu | 8 | 47 | 12.73 |
| Yijia Zhang | 9 | 4 | 5.47 |