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Text mining approaches for knowledge extraction from biomedical literature

Text mining approaches for knowledge extraction from biomedical literature

Material type
학위논문
Personal Author
이규범 李圭範
Title Statement
Text mining approaches for knowledge extraction from biomedical literature / Kyubum Lee
Publication, Distribution, etc
Seoul :   Graduate School, Korea University,   2017  
Physical Medium
99장 : 삽화, 도표 ; 26 cm
기타형태 저록
Text Mining Approaches for Knowledge Extraction from Biomedical Literature   (DCOLL211009)000000072561  
학위논문주기
학위논문(박사)-- 고려대학교 대학원: 컴퓨터·전파통신공학과, 2017. 2
학과코드
0510   6YD36   321  
General Note
지도교수: 姜在雨  
Bibliography, Etc. Note
참고문헌: 장 93-99
이용가능한 다른형태자료
PDF 파일로도 이용가능;   Requires PDF file reader(application/pdf)  
비통제주제어
Data Mining , Text Mining , BioNLP,,
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040 ▼a 211009 ▼c 211009 ▼d 211009
085 0 ▼a 0510 ▼2 KDCP
090 ▼a 0510 ▼b 6YD36 ▼c 321
100 1 ▼a 이규범 ▼g 李圭範
245 1 0 ▼a Text mining approaches for knowledge extraction from biomedical literature / ▼d Kyubum Lee
260 ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2017
300 ▼a 99장 : ▼b 삽화, 도표 ; ▼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 93-99
530 ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf)
653 ▼a Data Mining ▼a Text Mining ▼a BioNLP
776 0 ▼t Text Mining Approaches for Knowledge Extraction from Biomedical Literature ▼w (DCOLL211009)000000072561
900 1 0 ▼a 강재우 ▼g 姜在雨, ▼e 지도교수
945 ▼a KLPA

Electronic Information

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Text mining approaches for knowledge extraction from biomedical literature (52회 열람)
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Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Stacks(Thesis)/ Call Number 0510 6YD36 321 Accession No. 123055717 Availability Available Due Date Make a Reservation Service B M

Contents information

Abstract

As next generation sequencing techniques and high-throughput biomedical experiments continue to advance, the amount of biomedical big data continues to grow. In this era of precision medicine, it is becoming increasingly important to collect, manage, and utilize biomedical big data. However, much of the important knowledge is still published and shared in natural language form. Literature databases such as PubMed and PubMed Central collect biomedical literature daily, but knowledge in natural language form is still not the best format for utilizing or analyzing biomedical knowledge. Experts in each domain aim to build and reorganize knowledge bases on topics of their interest by manual curation; however, it is infeasible to read all the publications, and manually collect and organize the information. To overcome such limitations, text mining techniques for extracting knowledge and constructing knowledge bases can be used.
We have conducted a series of research studies on knowledge extraction from the literature, automatic curation, organizing, and utilization of knowledge.
In the first study, we aim to find genomic mutations in cancer-related literature and to create a corpus called BRONCO that contains related genes, diseases, drugs, and cell lines. This corpus can be used as a learning and evaluation data set for extracting information using text mining. Utilizing this corpus, we compare and analyze the performance of existing text mining technologies and tools.
In the second study, we use this corpus to construct an algorithm that extracts information from documents. Whereas traditional text mining techniques focus on target text, I utilize biomedical search engines to extract relationships between biomedical objects. I also used convolutional neural network (CNN) for relation classification method.
In the third study, we build an application that shows important information extracted from biomedical literature and provides more related knowledge to users. To make text mining results more accessible and available to readers who use PubMed or PubMed Central, we construct a biomedical entity network for each document using texts and other various sources. 
This dissertation introduces a series of processes that use text mining to extract knowledge from biomedical literature.

Table of Contents

Abstract
Chapter 1.Introduction
Chapter 2.Construction of biomedical entity relation corpus
Chapter 3.Mutation-Gene-Drug relations extraction method
Chapter 4.Building an application that translates text articles into networks
Chapter 5.Conclusion
Bibliography

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