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학술지 Classifying Biomedical Literature Providing Protein Function Evidence
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저자
임준호, 이규철
발행일
201508
출처
ETRI Journal, v.37 no.4, pp.813-823
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.15.0114.0041
협약과제
13VC2600, 신체변화 모니터링 맞춤형 사이버 주치의 정밀 건강관리 시스템 개발, 박찬용
초록
Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features-protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F-measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated-information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.
키워드
Automatic text summarization, Document classification, Information retrieval, Protein function evidence
KSP 제안 키워드
Automatic Text Summarization, Biomedical literature, Case studies, Classification Performance, Classification system, F-measure, Information retrieval(IR), Protein Function, automatic summarization, document classification