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BMC Bioinformatics. 2016 Sep 20;17:386. doi: 10.1186/s12859-016-1249-5.

A corpus for plant-chemical relationships in the biomedical domain.

Author information

1
School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea.
2
Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon, Republic of Korea.
3
School of Information and Communications, Gwangju Institute of Science and Technology, Chemdangwagi-ro, Gwangju, Republic of Korea. hyunjulee@gist.ac.kr.

Abstract

BACKGROUND:

Plants are natural products that humans consume in various ways including food and medicine. They have a long empirical history of treating diseases with relatively few side effects. Based on these strengths, many studies have been performed to verify the effectiveness of plants in treating diseases. It is crucial to understand the chemicals contained in plants because these chemicals can regulate activities of proteins that are key factors in causing diseases. With the accumulation of a large volume of biomedical literature in various databases such as PubMed, it is possible to automatically extract relationships between plants and chemicals in a large-scale way if we apply a text mining approach. A cornerstone of achieving this task is a corpus of relationships between plants and chemicals.

RESULTS:

In this study, we first constructed a corpus for plant and chemical entities and for the relationships between them. The corpus contains 267 plant entities, 475 chemical entities, and 1,007 plant-chemical relationships (550 and 457 positive and negative relationships, respectively), which are drawn from 377 sentences in 245 PubMed abstracts. Inter-annotator agreement scores for the corpus among three annotators were measured. The simple percent agreement scores for entities and trigger words for the relationships were 99.6 and 94.8 %, respectively, and the overall kappa score for the classification of positive and negative relationships was 79.8 %. We also developed a rule-based model to automatically extract such plant-chemical relationships. When we evaluated the rule-based model using the corpus and randomly selected biomedical articles, overall F-scores of 68.0 and 61.8 % were achieved, respectively.

CONCLUSION:

We expect that the corpus for plant-chemical relationships will be a useful resource for enhancing plant research. The corpus is available at http://combio.gist.ac.kr/plantchemicalcorpus .

KEYWORDS:

Chemical; Corpus; Data mining; Medicine; Natural language processing; Natural product; Plant; Text mining

PMID:
27650402
PMCID:
PMC5029005
DOI:
10.1186/s12859-016-1249-5
[Indexed for MEDLINE]
Free PMC Article

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