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论文类型：会议论文

发表时间：2009-09-24

收录刊物：EI、CPCI-S、SCIE、Scopus

页面范围：242-248

关键字：PPI; SVM; KNN; SVM-KNN; unbalanced data distribution

摘要：This paper presents a novel method to extract Protein-Protein Interaction (PPI) information from biomedical literatures based on Support Vector Machine (SVM) and K Nearest Neighbors (KNN). The two protein names, words between two proteins, words surrounding two proteins, keyword between or among the surrounding words of two protein names, ExpDistance based on word distance of two proteins, Pro Distance of two proteins in a protein pair are extracted as features of the vectors. A model based on SVM is setup to extract the interaction. To improve the accuracy of SVM classifier, KNN method is introduced. Furthermore, to fit the unbalanced data distribution, a modified SVM-KNN classifier is proposed. Experiments conducted on BC-PPI corpus show that our modified SVM-KNN classifier with the two distance features is efficient at extracting Protein-Protein Interaction information. The recall, precision and F-score are 87.2%, 82.4%, 84.7% respectively which outperform most of the state-of-the-art systems.