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This article is part of the supplement: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics

Open Access Proceedings

HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features

Richard Tzong-Han Tsai1*, Po-Ting Lai1, Hong-Jie Dai23, Chi-Hsin Huang2, Yue-Yang Bow2, Yen-Ching Chang2, Wen-Harn Pan4 and Wen-Lian Hsu2

Author Affiliations

1 Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China

2 Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan, Republic of China

3 Department of Computer Science, National Tsing-Hua University, HsinChu, Taiwan, Republic of China

4 Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan, Republic of China

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BMC Bioinformatics 2009, 10(Suppl 15):S9  doi:10.1186/1471-2105-10-S15-S9

Published: 3 December 2009

Abstract

Background

The genetic factors leading to hypertension have been extensively studied, and large numbers of research papers have been published on the subject. One of hypertension researchers' primary research tasks is to locate key hypertension-related genes in abstracts. However, gathering such information with existing tools is not easy: (1) Searching for articles often returns far too many hits to browse through. (2) The search results do not highlight the hypertension-related genes discovered in the abstract. (3) Even though some text mining services mark up gene names in the abstract, the key genes investigated in a paper are still not distinguished from other genes. To facilitate the information gathering process for hypertension researchers, one solution would be to extract the key hypertension-related genes in each abstract. Three major tasks are involved in the construction of this system: (1) gene and hypertension named entity recognition, (2) section categorization, and (3) gene-hypertension relation extraction.

Results

We first compare the retrieval performance achieved by individually adding template features and position features to the baseline system. Then, the combination of both is examined. We found that using position features can almost double the original AUC score (0.8140vs.0.4936) of the baseline system. However, adding template features only results in marginal improvement (0.0197). Including both improves AUC to 0.8184, indicating that these two sets of features are complementary, and do not have overlapping effects. We then examine the performance in a different domain--diabetes, and the result shows a satisfactory AUC of 0.83.

Conclusion

Our approach successfully exploits template features to recognize true hypertension-related gene mentions and position features to distinguish key genes from other related genes. Templates are automatically generated and checked by biologists to minimize labor costs. Our approach integrates the advantages of machine learning models and pattern matching. To the best of our knowledge, this the first systematic study of extracting hypertension-related genes and the first attempt to create a hypertension-gene relation corpus based on the GAD database. Furthermore, our paper proposes and tests novel features for extracting key hypertension genes, such as relative position, section, and template features, which could also be applied to key-gene extraction for other diseases.