Mining protein interaction from biomedical literature with relation kernel method

Authors:
Jae-Hong Eom;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
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
Cited 1

Text classification using string kernels

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Kernel methods for relation extraction

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Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering

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

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Abstract

Many interaction data still exist only in the biomedical literature and they require much effort to construct well-structured data. Discovering useful knowledge from large collections of papers is becoming more important for efficient biological and biomedical researches as genomic research advances. In this paper, we present a relation kernel-based interaction extraction method to extract knowledge efficiently. We extract protein interactions of from text documents with relation kernel and Yeast was used as an example target organism. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. The proposed method only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 79.0% and precision rate of 80.8% for protein interaction extraction from biomedical document without full parsing efforts.