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A multilayer networks analysis for mining quantification rules from big proteomics data

Title
A multilayer networks analysis for mining quantification rules from big proteomics data
Translated Title
빅 데이터에 기반하여 단백질체학 데이터에서의 수량화 규칙을 찾기 위한 다층 네트워크 구축과 분석
Authors
Jin, Su Hyoen
DGIST Authors
Jin, Su Hyoen; Kim, Min Soo; Hwang, Dae Hee
Advisor(s)
Kim, Min Soo
Co-Advisor(s)
Hwang, Dae Hee
Issue Date
2017
Available Date
2017-01-18
Degree Date
2017. 2
Type
Thesis
Keywords
BioinformaticsProteomicsMultilayer network
Abstract
Data modeling is important to understand and obtain the information from the data. Diverse designs can be developed for finding hidden information. Existing research in proteomics is limited in data modeling since only analysis of Protein–protein interaction (PPI) network is usually conducted. Here, we present a new approach for finding rules and bases to understand mechanisms of protein function. We build the multilayer network for integrating bottom-up proteomics data which is named TLP network. TLP network contains diverse biological information including the peptide expression data, and PTMs as well as Protein–protein interactions (PPIs). TLP network is expected to answer a wide range of questions in proteomics research area. ⓒ 2017 DGIST
Table Of Contents
1. Modification 1 -- 1.1 Difficulty to identify the Differentially Expressed Proteins 1 -- 1.2 Data integration to find rules from big data 2 -- 2. Introduction 4 -- 2.1 Bioinformatics 4 -- 2.2 Proteomics 4 -- 2.3 Post-translational modification (PTM) 5 -- 2.4 Bottom-up proteomics 6 -- 2.5 Peptide identification 7 -- 2.6 Quantitative proteomics 9 -- 2.7 Proteomics data repositories 11 -- 2.8 Network analysis 12 -- 3. Three Layer proteomics (TLP) network 13 -- 3.1 Data collection 13 -- 3.2 Process of peptide identification 14 -- 3.3 Process of peptide quantification 16 -- 3.4 Network architecture 18 -- 3.5 Example of graph construction proces 25 -- 3.6 Network size 28 -- 4. Analysis of TLP network 31 -- 5. Conclusion 35 -- REFERENCE 36
URI
http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002329752
http://hdl.handle.net/20.500.11750/1524
DOI
10.22677/thesis.2329752
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Files:
Collection:
Information and Communication EngineeringThesesMaster


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