Extracting chemical–protein relations with ensembles of SVM and deep learning models

Yifan Peng, Anthony Rios, Ramakanth Kavuluru, and Zhiyong Lu

Database, Volume 2018, 1 January 2018

Abstract:

Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical–protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network. Their output is combined using majority voting or stacking for final predictions. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the highest performance in the task during the 2017 challenge.

[Link]

@article{doi:10.1093/database/bay073,
author = {Peng, Yifan and Rios, Anthony and Kavuluru, Ramakanth and Lu, Zhiyong},
title = {Extracting chemical–protein relations with ensembles of SVM and deep learning models},
journal = {Database},
volume = {2018},
number = {},
pages = {bay073},
year = {2018},
doi = {10.1093/database/bay073},
URL = {http://dx.doi.org/10.1093/database/bay073},
eprint = {/oup/backfile/content_public/journal/database/2018/10.1093_database_bay073/1/bay073.pdf}
}