title: A graph kernel based on context vectors for extracting drug-drug interactions.
abstract: The clinical recognition of drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can't yet adequately express long and complicated sentences. In this paper, we present an effective graph kernel which makes full use of different types of contexts to identify DDIs from biomedical literature. In our approach, the relations among long-range words, in addition to close-range words, are obtained by the graph representation of a parsed sentence. Context vectors of a vertex, an iterative vectorial representation of all labeled nodes adjacent and nonadjacent to it, adequately capture the direct and indirect substructures' information. Furthermore, the graph kernel considering the distance between context vectors is used to detect DDIs. Experimental results on the DDIExtraction 2013 corpus show that our system achieves the best detection and classification performance (F-score) of DDIs (81.8 and 68.4, respectively). Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10.7 and 12.2 in detection and classification, respectively.
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China; College of Software, Dalian JiaoTong University, Dalian, China. Electronic address: weizheng@mail.dlut.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China. Electronic address: hflin@dlut.edu.cn.
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Zhehuan
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Yijia
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Wang
mesh_terms: D057225:Data Mining; D004347:Drug Interactions; D006801:Humans; D016239:MEDLINE; D011642:Publications; D060388:Support Vector Machine
keywords: Context vector; Drug–drug interactions; Equivalent class; Graph kernel