Extraction of Entity Interactions Based on Multiple Feature Fusion Linear Kernel SVM Approach
Wei Xing1,2, Hu Dehua2*, Yi Minhan2, Chang Xuelian3, Yang Xiaodi3, Zhu Wenjie1
1(School of Basic Courses, Bengbu Medical College,Bengbu 233003, Anhui, China) 2(Institute of Information Security and Big Data, Central South University, Changsha 410083, China) 3 (School of Basic Medicine, Bengbu Medical College, Bengbu 233003, Anhui, China)
Abstract Improving the performance of interaction mining algorithm can help to explore some innovative ideas in the biomedical literature. We proposed a novel feature-based linear kernel support vector machine (SVM) approach to extract and investigate the interactions between diabetes mellitus, genes and drugs. We elaborated the five types of features (entity, entity pair, dependency graph, parse tree, noun phrase-constrained coordination) used, including two novel features, word pair and noun phrase-constrained coordination features. Then 173 interactions between 13 kinds of diabetes mellitus and 23 genes, 79 interactions between 13 kinds of diabetes mellitus and 26 drugs, 159 interactions between 18 genes and 17 genes, 619 interactions between 8 kinds of diabetes mellitus, 23 genes and 26 drugs were ontained. And 27 new entity interactions were predicted. After that we constructed the interaction network of the disease-gene, gene-drug, and disease-gene-drug. The experimental results showed that the proposed method was comparable with the algorithms used in CoPub (0.710), PubGene (0.609), FBK-irst (0.547, 0.800) and WBI (0.510, 0.759), the highest accuracy increased by about 5% (0.847 vs 0.800, and the minimum increased by over 20% (0.742 vs 0.510), which provided perspectives for applications of biomedical big data.
Wei Xing,Hu Dehua,Yi Minhan, et al. Extraction of Entity Interactions Based on Multiple Feature Fusion Linear Kernel SVM Approach[J]. Chinese Journal of Biomedical Engineering, 2018, 37(4): 451-460.
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