NDLI: Protein-protein interaction extraction from biomedical literatures based on modified SVM-KNN

Content Provider	IEEE Xplore Digital Library
Author	Lishuang Li Linmei Jing Degen Huang
Copyright Year	2009
Description	Author affiliation: Dalian University of Technology, Dalian, Liaoning, China (Lishuang Li; Linmei Jing; Degen Huang)
Abstract	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, ProDistance 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.
Starting Page	1
Ending Page	7
File Size	168834
Page Count	7
File Format	PDF
ISBN	9781424445387
DOI	10.1109/NLPKE.2009.5313735
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PPI Biomedical computing KNN SVM Data mining Nearest neighbor searches Proteins Support vector machines Learning systems SVM-KNN unbalanced data distribution Support vector machine classification Production Feature extraction Bioinformatics
Content Type	Text
Resource Type	Article

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