TY - GEN
T1 - Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory
AU - Hsieh, Yu Lun
AU - Chang, Yung-Chun
AU - Chang, Nai Wen
AU - Hsu, Wen Lian
PY - 2017
Y1 - 2017
N2 - Accurate identification of protein-protein interaction (PPI) helps biomedical researchers to quickly capture crucial information in literatures. This work proposes a recurrent neural network (RNN) model to identify PPIs. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that RNN outperforms state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also indicates that RNN is robust even when trained on data from different domains. These results suggest that RNN effectively captures semantic relationships among proteins without any feature engineering.
AB - Accurate identification of protein-protein interaction (PPI) helps biomedical researchers to quickly capture crucial information in literatures. This work proposes a recurrent neural network (RNN) model to identify PPIs. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that RNN outperforms state-of-the-art methods with relative improvements of 10% and 18%, respectively. Cross-corpus evaluation also indicates that RNN is robust even when trained on data from different domains. These results suggest that RNN effectively captures semantic relationships among proteins without any feature engineering.
UR - https://aclanthology.coli.uni-saarland.de/papers/I17-2041/i17-2041
UR - http://ijcnlp2017.org/site/page.aspx?pid=172&sid=1133&lang=en
M3 - Conference contribution
SP - 240
EP - 245
BT - Proceedings of the Eighth International Joint Conference on Natural Language Processing
PB - Asian Federation of Natural Language Processing
ER -