Development of a System for Extracting the Information of Candidate Tumor Markers Reported in Biomedical Literatures

Jeong-Min Chae; Heung-Bum Oh; Sung-Eun Choi; Choong-Hwan Cha; Myung-Hee Kim; Soon-Young Jung

doi:10.3343/kjlm.2008.28.1.79

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Chae, Oh, Choi, Cha, Kim, and Jung: Development of a System for Extracting the Information of Candidate Tumor Markers Reported in Biomedical Literatures

Original Article

Korean J Leg Med 2008;28(1):79-87.

Published online: 14 February 2008

DOI: https://doi.org/10.3343/kjlm.2008.28.1.79

Development of a System for Extracting the Information of Candidate Tumor Markers Reported in Biomedical Literatures

Jeong-Min Chae¹, Heung-Bum Oh, M.D.², Sung-Eun Choi², Choong-Hwan Cha, M.D.², Myung-Hee Kim, M.D.², Soon-Young Jung, Ph.D.¹

¹Department of Computer Science Education, Korea University, Seoul, Korea

²Department of Laboratory Medicine, University of Ulsan College of Medicine and Asan Medical Center, Seoul, Korea

Received 16 August 200712 November 2007 Accepted 15 November 2007

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Since the human genome project was completed in 2003, there have been numerous reports on cancer and related markers. This study was aimed to develop a system to extract automatically information regarding the relationship between cancer and tumor markers from biomedical literatures.

Methods

Named entities of tumor markers were recognized by both a dictionary-based method and machine learning technology of the support vector machine. Named entities of cancers were recognized by the MeSH dictionary.

Results

Relational and filtering keywords were selected after annotating 160 abstracts from PubMed. Relational information was extracted only when one of the relational keywords was in an appropriate position along the parse tree of a sentence with both tumor marker and disease entities. The performance of the system developed in this study was evaluated with another set of 77 abstracts. With the relational and filtering keyword used in the system, precision was 94.38% and recall was 66.14%, while without the expert knowledge precision was 49.16% and recall was 69.29%.

Conclusions

We developed a system that can extract relational information between a tumor and its markers by incorporating expert knowledge into the system. The system exploiting expert knowledge would serve as a reference when developing another information extraction system in various medical fields.

Keywords: Tumor, Tumor marker, Information extraction

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Fig. 1.

System overview for the relational information extraction. (A) Named entity recognition module. (B) Information extraction module.

Fig. 2.

Named entity recognition (NER) module by support vector machine (SVM)

Fig. 3.

Sentence annotation by medical experts for extracting relational and filtering keywords.

Fig. 4.

Postorder traversal algorithm for extracting relational information using a parse tree.

Fig. 5.

Example of extracted information.

Table 1.

Recognizing methods for named biological entities

Named entities	Recognizing methods
Tumor marker	Suppot vector machine, NCI Thesaurus
Tumor name (disease)	MeSH
Relation keywords	Direct extraction by medical experts
Filtering keywords	Direct extraction by medical experts

NCI Thesaurus (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do).

Medical Subject Heading (MeSH) (http://www.nlm.nih.gov/mesh/).

Table 2.

An example of relation and filtering keywords for extracting information relating between tumors and tumor markers

Type	Keyword
Relation	marker, biomarker, associate, expression, coexpression, overexpression, correlate, distinguish, elevation, factor, identification, increase, indicator, parameter, screening tool, upregulation, usefulness, utility, value
Filtering	additional studies, aim, analysed, antibody, assessed, cell lines, clarify, compared, design, evaluate, examined, further study, hypothesis, immunochemical, investigate, measured, methods, no studies, objective, performed, studied, tested, unclear, unknown, variable, whether

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