Open AccessIntegrating Semantic Information into Multiple Kernels for Protein-Protein Interaction Extraction from Biomedical Literatures
Author(s) -
Lishuang Li,
Panpan Zhang,
Tianfu Zheng,
Hongying Zhang,
Zhenchao Jiang,
Degen Huang
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0091898
Subject(s) - computer science , tree kernel , semantic similarity , wordnet , artificial intelligence , kernel (algebra) , graph kernel , context (archaeology) , machine learning , information extraction , support vector machine , kernel method , tree (set theory) , similarity (geometry) , radial basis function kernel , pattern recognition (psychology) , natural language processing , mathematics , image (mathematics) , paleontology , mathematical analysis , combinatorics , biology
Protein-Protein Interaction (PPI) extraction is an important task in the biomedical information extraction. Presently, many machine learning methods for PPI extraction have achieved promising results. However, the performance is still not satisfactory. One reason is that the semantic resources were basically ignored. In this paper, we propose a multiple-kernel learning-based approach to extract PPIs, combining the feature-based kernel, tree kernel and semantic kernel. Particularly, we extend the shortest path-enclosed tree kernel (SPT) by a dynamic extended strategy to retrieve the richer syntactic information. Our semantic kernel calculates the protein-protein pair similarity and the context similarity based on two semantic resources: WordNet and Medical Subject Heading (MeSH). We evaluate our method with Support Vector Machine (SVM) and achieve an F-score of 69.40% and an AUC of 92.00%, which show that our method outperforms most of the state-of-the-art systems by integrating semantic information.