Multichannel Convolutional Neural Network for Biological Relation Extraction

By Chanqin Quan, Lei Hua, Xiao Sun, Wenjun Bai. 2016

140 citations recorded by the source corpus.

The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Makoto Miwa
2. Zhiyong Lu
3. Yutaka Sasaki
4. Masaki Asada
5. Robert Leaman
6. Claire Cardie
7. Mohit Bansal
8. Arzoo Katiyar

In Molecular Biology

1. Zhiyong Lu
2. Robert Leaman
3. Chih-Hsuan Wei
4. Anita M. Preininger
5. Fei Wang
6. Rezarta Islamaj Dogan

Citations in the corpus

• Deep learning for extracting protein-protein interactions from biomedical literature (2017, methodology): “For PPI extraction, convolutional neural networks (CNN) have been adopted and applied effectively (Zeng et al., 2014; Quan et al., 2016; Hua and Quan, 2016).”
• Extracting Drug-Drug Interactions with Attention CNNs (2017, methodology): “In the comparison without negative instance fil-tering, our model outperformed the existing CNN models (Liu et al., 2016; Quan et al., 2016; Zhao et al., 2016).”