High-precision bio-molecular event extraction from text using parallel binary classifiers
- Author
- Sofie Van Landeghem (UGent) , Bernard De Baets (UGent) , Yves Van de Peer (UGent) and Yvan Saeys (UGent)
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- Project
- Abstract
- We have developed a machine learning framework to accurately extract complex genetic interactions from text. Employing type-specific classifiers, this framework processes research articles to extract various biological events. Subsequently, the algorithm identifies regulation events that take other events as arguments, allowing a nested structure of predictions. All predictions are merged into an integrated network, useful for visualization and for deduction of new biological knowledge. In this paper, we discuss several design choices for an event-based extraction framework. These detailed studies help improving on existing systems, which is illustrated by the relative performance gain of 10% of our system compared to the official results in the recent BioNLP'09 Shared Task. Our framework now achieves state-of-the-art performance with 37.43 recall, 54.81 precision and 44.48 F-score. We further present the first study of feature selection for bio-molecular event extraction from text. While producing more cost-effective models, feature selection can also lead to a better insight into the complexity of the challenge. Finally, this paper tries to bridge the gap between theoretical relation extraction from text and experimental work on bio-molecular interactions by discussing interesting opportunities to employ event-based text mining tools for real-life tasks such as hypothesis generation, database curation and knowledge discovery.
- Keywords
- machine learning, BioNLP, text mining
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1993627
- MLA
- Van Landeghem, Sofie, et al. “High-Precision Bio-Molecular Event Extraction from Text Using Parallel Binary Classifiers.” COMPUTATIONAL INTELLIGENCE, vol. 27, no. 4, 2011, pp. 645–64, doi:10.1111/j.1467-8640.2011.00403.x.
- APA
- Van Landeghem, S., De Baets, B., Van de Peer, Y., & Saeys, Y. (2011). High-precision bio-molecular event extraction from text using parallel binary classifiers. COMPUTATIONAL INTELLIGENCE, 27(4), 645–664. https://doi.org/10.1111/j.1467-8640.2011.00403.x
- Chicago author-date
- Van Landeghem, Sofie, Bernard De Baets, Yves Van de Peer, and Yvan Saeys. 2011. “High-Precision Bio-Molecular Event Extraction from Text Using Parallel Binary Classifiers.” COMPUTATIONAL INTELLIGENCE 27 (4): 645–64. https://doi.org/10.1111/j.1467-8640.2011.00403.x.
- Chicago author-date (all authors)
- Van Landeghem, Sofie, Bernard De Baets, Yves Van de Peer, and Yvan Saeys. 2011. “High-Precision Bio-Molecular Event Extraction from Text Using Parallel Binary Classifiers.” COMPUTATIONAL INTELLIGENCE 27 (4): 645–664. doi:10.1111/j.1467-8640.2011.00403.x.
- Vancouver
- 1.Van Landeghem S, De Baets B, Van de Peer Y, Saeys Y. High-precision bio-molecular event extraction from text using parallel binary classifiers. COMPUTATIONAL INTELLIGENCE. 2011;27(4):645–64.
- IEEE
- [1]S. Van Landeghem, B. De Baets, Y. Van de Peer, and Y. Saeys, “High-precision bio-molecular event extraction from text using parallel binary classifiers,” COMPUTATIONAL INTELLIGENCE, vol. 27, no. 4, pp. 645–664, 2011.
@article{1993627,
abstract = {{We have developed a machine learning framework to accurately extract complex genetic interactions from text. Employing type-specific classifiers, this framework processes research articles to extract various biological events. Subsequently, the algorithm identifies regulation events that take other events as arguments, allowing a nested structure of predictions. All predictions are merged into an integrated network, useful for visualization and for deduction of new biological knowledge. In this paper, we discuss several design choices for an event-based extraction framework. These detailed studies help improving on existing systems, which is illustrated by the relative performance gain of 10% of our system compared to the official results in the recent BioNLP'09 Shared Task. Our framework now achieves state-of-the-art performance with 37.43 recall, 54.81 precision and 44.48 F-score. We further present the first study of feature selection for bio-molecular event extraction from text. While producing more cost-effective models, feature selection can also lead to a better insight into the complexity of the challenge. Finally, this paper tries to bridge the gap between theoretical relation extraction from text and experimental work on bio-molecular interactions by discussing interesting opportunities to employ event-based text mining tools for real-life tasks such as hypothesis generation, database curation and knowledge discovery.}},
author = {{Van Landeghem, Sofie and De Baets, Bernard and Van de Peer, Yves and Saeys, Yvan}},
issn = {{0824-7935}},
journal = {{COMPUTATIONAL INTELLIGENCE}},
keywords = {{machine learning,BioNLP,text mining}},
language = {{eng}},
number = {{4}},
pages = {{645--664}},
title = {{High-precision bio-molecular event extraction from text using parallel binary classifiers}},
url = {{http://dx.doi.org/10.1111/j.1467-8640.2011.00403.x}},
volume = {{27}},
year = {{2011}},
}
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