RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재 SCIE SCOPUS

      의생물학 문헌에 보고된 후보 암표지자 정보추출시스템 개발 = Development of a System for Extracting the Information of Candidate Tumor Markers Reported in Biomedical Literatures

      한글로보기

      https://www.riss.kr/link?id=A101631224

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual 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. (Korean J Lab Med 2008;28:79-87)
      번역하기

      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 ...

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

      더보기

      참고문헌 (Reference)

      1 신해림, "우리나라 암등록사업과 암통계" 대한암예방학회지 9 (9): 49-55, 2004

      2 Novichkova S, "a natural language processing engine for MEDLINE abstracts" 19 : 1699-1706, 2003

      3 Krauthammer M, "Using BLAST for identifying gene and protein names in journal articles" 259 : 245-252, 2000

      4 Collins FS, "US National Human Genome Research Institute. A vision for the future of genomics research" 422 : 835-847, 2003

      5 Kazama J, "Tunning support vector machines for biomedical named entity recognition. In: Association for Computational Linguistics" 1-8, 2002

      6 Chae JM, "Tumor marker information extraction system. http://medtextmining.net/ (Updated on Aug 2006)"

      7 Tanabe L, "Tagging gene and protein names in biomedical text" 18 : 1124-1132, 2002

      8 Zhou G, "Recognizing names in biomedical texts: a machine learning approach" 20 : 1178-1190, 2004

      9 Hernandez J, "Prostate-specific antigen: a review of the validation of the most commonly used cancer biomarker" 101 : 894-904, 2004

      10 Marsh SG, "Nomenclature for factors of the HLA system" 65 : 301-369, 2005

      1 신해림, "우리나라 암등록사업과 암통계" 대한암예방학회지 9 (9): 49-55, 2004

      2 Novichkova S, "a natural language processing engine for MEDLINE abstracts" 19 : 1699-1706, 2003

      3 Krauthammer M, "Using BLAST for identifying gene and protein names in journal articles" 259 : 245-252, 2000

      4 Collins FS, "US National Human Genome Research Institute. A vision for the future of genomics research" 422 : 835-847, 2003

      5 Kazama J, "Tunning support vector machines for biomedical named entity recognition. In: Association for Computational Linguistics" 1-8, 2002

      6 Chae JM, "Tumor marker information extraction system. http://medtextmining.net/ (Updated on Aug 2006)"

      7 Tanabe L, "Tagging gene and protein names in biomedical text" 18 : 1124-1132, 2002

      8 Zhou G, "Recognizing names in biomedical texts: a machine learning approach" 20 : 1178-1190, 2004

      9 Hernandez J, "Prostate-specific antigen: a review of the validation of the most commonly used cancer biomarker" 101 : 894-904, 2004

      10 Marsh SG, "Nomenclature for factors of the HLA system" 65 : 301-369, 2005

      11 Park JC, "Named entity recognition in; Text mining for biology and biomedicine" Artech House 121-142, 2006

      12 Jensen LJ, "Literature mining for the biologist: from information retrieval to biological discovery" 7 : 119-129, 2006

      13 Bodenreider O, "Lexical, terminological, and ontological resources for bilogical text mining in: Text mining for biology and biomedicine" Artech House 43-67, 2006

      14 Ananiadou S, "Introduction in: Text mining for biology and biomedicine" Artech House 1-12, 2006

      15 McNaught J, "Information extraction in: Text Mining for biology and biomedicine" Artech House 143-177, 2006

      16 Collins M, "Head-Driven Statistical Models for Natural Language Parsing" Pennsylvania Univ. 1995

      17 Friedman C, "GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles" 17 (17): S74-S82, 2001

      18 Kim JD, "GENIA corpus- semantically annotated corpus for bio-textmining" 19 (19): i180-i182, 2003

      19 Temkin JM, "Extraction of protein interaction information from unstructured text using a context-free grammar" 19 : 2046-2053, 2003

      20 Hatzivassiloglou V, "Disambiguating proteins, genes, and RNA in text: a machine learning approach" 17 (17): S97-S106, 2001

      21 Proux D, "Detecting Gene Symbols and Names in Biological Texts: A First Step toward Pertinent Information Extraction" 9 : 72-80, 1998

      22 Herbst RS, "Clinical Cancer Advances 2005: major research advances in cancer treatment, prevention, and screening--a report from the American Society of Clinical Oncology" 24 : 190-205, 2006

      23 Lee KJ, "Biomedical named entity recognition using two-phase model based on SVMs" 37 : 436-447, 2004

      24 Ananiadou S, "Automatic terminology management in biomedicine in: Text mining for biology and biomedicine" Artech House, Inc. 67-98, 2006

      25 Horn F, "Automated extraction of mutation data from the literature: application of MuteXt to G protein-coupled receptors and nuclear hormone receptors" 20 : 557-568, 2004

      26 Ono T, "Automated extraction of information on protein-protein interactions from the biological literature" 17 : 155-161, 2000

      27 Cristianini N, "An introduction to support vector machines and other kernel based learning methods" Cambridge University Press 2000

      더보기

      동일학술지(권/호) 다른 논문

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2012-05-21 학술지명변경 한글명 : The Korean Journal of Laboratory Medicine -> Annals of Laboratory Medicine
      외국어명 : The Korean Journal of Laboratory Medicine -> Annals of Laboratory Medicine
      KCI등재
      2011-01-01 평가 학술지 분리 (기타) KCI등재
      2010-06-29 학술지명변경 한글명 : 대한진단검사의학회지 -> The Korean Journal of Laboratory Medicine KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.51 0.18 1.15
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.91 0.81 0.458 0.08
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼