title: Multichannel Convolutional Neural Network for Biological Relation Extraction.
abstract: 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 overallf-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% onf-scores.
authors.affiliation: Graduate School of System Informatics, Kobe University, Kobe, Japan.
authors.forename: Chanqin
authors.initials: C
authors.lastname: Quan
authors.affiliation: Department of Computer and Information Science, Hefei University of Technology, Hefei, China.
authors.forename: Lei
authors.identifier: 0000-0003-2630-4080
authors.initials: L
authors.lastname: Hua
authors.affiliation: Department of Computer and Information Science, Hefei University of Technology, Hefei, China.
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
authors.affiliation: Graduate School of System Informatics, Kobe University, Kobe, Japan.
authors.forename: Wenjun
authors.initials: W
authors.lastname: Bai
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000379:methods*; D057225:Data Mining; D004347:Drug Interactions; D000069550:Machine Learning; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping; D060388:Support Vector Machine