HypertenGene: extracting key hypertension genes from biomedical literature with position and automatically-generated template features.
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 (0.8140 vs.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.


CONCLUSIONS
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.
Tsai
Richard Tzong-Han
RT
Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, Republic of China. thtsai@saturn.yzu.edu.tw
Lai
Po-Ting
PT
Dai
Hong-Jie
HJ
Huang
Chi-Hsin
CH
Bow
Yue-Yang
YY
Chang
Yen-Ching
YC
Pan
Wen-Harn
WH
Hsu
Wen-Lian
WL
D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D019277:Entropy; D005796:Genes; D006973:Hypertension / Q000235:genetics*; D039781:PubMed; D012984:Software