A tree kernel-based method for protein-protein interaction mining from biomedical literature

Authors:
Jae-Hong Eom;Sun Kim;Seong-Hwan Kim;Byoung-Tak Zhang
Affiliations:
Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Year:
2006

Citing 9
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
Head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing
Text classification using string kernels

The Journal of Machine Learning Research
Word sequence kernels

The Journal of Machine Learning Research
Kernel methods for relation extraction

The Journal of Machine Learning Research
New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Discovering patterns to extract protein--protein interactions from full texts

Bioinformatics
Discovering patterns to extract protein–protein interactions from the literature: Part II

Bioinformatics

Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

As genomic research advances, the knowledge discovery from a large collection of scientific papers becomes more important for efficient biological and biomedical research. Even though current databases continue to update new protein-protein interactions, valuable information still remains in biomedical literature. Thus data mining techniques are required to extract the information. In this paper, we present a tree kernel-based method to mine protein-protein interactions from biomedical literature. The tree kernel is designed to consider grammatical structures for given sentences. A support vector machine classifier is combined with the tree kernel and trained on predefined interaction corpus and set of interaction patterns. Experimental results show that the proposed method gives promising results by utilizing the structure patterns.