Multichannel Convolutional Neural Network for Biological Relation Extraction

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 s...

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Bibliographic Details
Published in:BioMed research international 2016-01, Vol.2016 (2016), p.1-10
Main Authors: Bai, Wenjun, Sun, Xiao, Hua, Lei, Quan, Chanqin
Format: Article
Language:English
Subjects:
Algorithms
Biomedical Research - methods
Classification
Data Mining
Drug Interactions
Machine Learning
Methods
Neural networks
Neural Networks (Computer)
Noise
Protein Interaction Mapping
Protein-protein interactions
Proteins
Semantics
Support Vector Machine
Technology application
Vocabularies & taxonomies
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Summary: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.
ISSN:2314-6133
2314-6141
DOI:10.1155/2016/1850404