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论文类型：期刊论文

发表时间：2011-10-01

发表刊物：PROTEOMICS

收录刊物：Scopus、SCIE、PubMed

卷号：11

期号：19,SI

页面范围：3811-3817

ISSN号：1615-9853

关键字：Bioinformatics; Information extraction; Multiple kernels learning; Protein-protein interaction; Support Vector Machines; Text mining

摘要：Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.