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Entity search methods for knowledge discovery from biomedical literature

Entity search methods for knowledge discovery from biomedical literature

Material type
학위논문
Personal Author
이선원 李善瑗
Title Statement
Entity search methods for knowledge discovery from biomedical literature / Sunwon Lee
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2017  
Physical Medium
iv, 51 장 : 삽화 ; 26 cm
기타형태 저록
Entity search methods for knowledge discovery from biomedical literature   (DCOLL211009)000000072071  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2017. 2
학과코드
0510   6YD36   322  
General Note
지도교수: 姜在雨  
Bibliography, Etc. Note
참고문헌: 장 47-51
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
검색 시스템 , 바이오인포매틱스 , 객체검색시스템,,
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007 ta
008 161229s2017 ulka bmAC 000c eng
040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6YD36 ▼c 322
100 1 ▼a 이선원 ▼g 李善瑗
245 1 0 ▼a Entity search methods for knowledge discovery from biomedical literature / ▼d Sunwon Lee
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2017
300 ▼a iv, 51 장 : ▼b 삽화 ; ▼c 26 cm
500 ▼a 지도교수: 姜在雨
502 1 ▼a 학위논문(박사)-- ▼b 고려대학교 대학원: ▼c 컴퓨터·전파통신공학과, ▼d 2017. 2
504 ▼a 참고문헌: 장 47-51
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a 검색 시스템 ▼a 바이오인포매틱스 ▼a 객체검색시스템
776 0 ▼t Entity search methods for knowledge discovery from biomedical literature ▼w (DCOLL211009)000000072071
900 1 0 ▼a Lee, Sun-won, ▼e
900 1 0 ▼a 강재우 ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

Electronic Information

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Entity search methods for knowledge discovery from biomedical literature (26회 열람)
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No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 322 Accession No. 123055719 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

As the volume of publications rapidly increases, searching for relevant information from the literature becomes more challenging. To complement standard search engines such as PubMed, it is desirable to have n advanced search tool that directly returns relevant biomedical entities such as targets, drugs, and mutations rather than a long list of articles. Some existing tools submit a query to PubMed and process retrieved abstracts to extract information at query time, resulting in a slow response time and limited coverage of only a fraction of the PubMed corpus. Other tools preprocess the PubMed corpus to speed up the response time; however, they are not constantly updated, and thus produce outdated results. Further, most existing tools cannot process sophisticated queries such as searches for mutations that co-occur with query terms in the literature. To address these problems, we introduce BEST, a biomedical entity search tool. BEST returns, as a result, a list of 10 different types of biomedical entities including genes, diseases, drugs, targets, transcription factors, miRNAs, and mutations that are relevant to a user’s query. To the best of our knowledge, BEST is the only system that processes free text queries and returns up-to-date results in real time including mutation information in the results. BEST is freely accessible at http://best.korea.ac.kr.

Table of Contents

Contents
Abstract
Contents i
List of Figures iii
List of Tables iv
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Methods 5
2.1 Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Dictionary based named entity recognition . . . . . . . . . . . . . . 7
2.1.2 Document-entity list pair indexing . . . . . . . . . . . . . . . . . . 9
2.2 Searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Document retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Entity scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1 Entity meta-information and index update policy . . . . . . . . . . 13
2.4 Indexing statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
i
3 Evaluation 16
3.1 Evaluation of biomedical entity extraction in BEST . . . . . . . . . . . . . 16
3.2 Machine learning based entity extraction test . . . . . . . . . . . . . . . . 17
3.3 PubTator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 BRONCO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.5 Comparing BEST with existing systems . . . . . . . . . . . . . . . . . . . 22
3.6 Evaluating the recency factor in BEST . . . . . . . . . . . . . . . . . . . . 25
4 System utilization examples 31
4.1 BEST as a Question Answering System . . . . . . . . . . . . . . . . . . . 31
4.1.1 Identifying mutations that confer resistance to drugs . . . . . . . . 34
4.1.2 Identifying alternative drugs that overcome acquired resistance . . 36
4.1.3 Identifying related genes in a pathway . . . . . . . . . . . . . . . . 37
4.2 BEST as a Literature Mining Engine in Biomedical Analysis . . . . . . . 39
4.2.1 Drug Signature Database (DSigDB) . . . . . . . . . . . . . . . . . 39
4.2.2 CLIP-GENE, a gene prioritization tool for knock out mouse model 40
4.2.3 ContextTRAP, a context-aware time-series RNA-seq analysis package 40
5 Limitations & Future works 42
6 Conclusion 44
Bibliography 47

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