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An Information Theoretic Algorithm for Mining and Ranking Phenotype-specific Sub-networks from Multi-class Gene Expression Data : 다중 클래스 유전자 발현 데이터에서 표현형 특이적 서브 네트워크 발굴 및 랭킹을 위한 정보 이론 기반 알고리즘
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 김선 | - |
dc.contributor.author | 박진우 | - |
dc.date.accessioned | 2017-07-14T03:03:11Z | - |
dc.date.available | 2017-07-14T03:03:11Z | - |
dc.date.issued | 2017-02 | - |
dc.identifier.other | 000000142139 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123215 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김선. | - |
dc.description.abstract | There have been extensive studies for inferring transcriptional network from omics data. However, how to utilize networks for specific research projects has not been well established. One of the main hurdles is lack of algorithms for mining biological sub-networks. Existing graph mining algorithms do not consider features of the transcriptional network and they are not effective to obtain biologically meaningful results. In this paper, we define the biological sub-network mining problem and present a new graph mining algorithm that mines and ranks phenotype specific sub-networks of transcriptional regulatory networks constructed from multi-class gene expression data. Our contributions in this paper on the computational side are two folds. First, we suggest a complete research paradigm of utilizing omics data to construct networks and then elucidate s ub-networks that distinguish phenotypes or disease states. Second, we developed an information theoretic algorithm for mining phenotype specific sub-networks. Our contribution on the bio/medical side is that our TF-module based analysis determined biological pathways (cell cycle: M-phase, cell adhesion molecules) related to the phenotype (breast tumor grade) by identifying activation/suppression of specific target genes (TGs) by the combination of multiple transcription factors (TFs). Expression levels of TGs clearly shows correlation between activation/suppression of these pathways and tumor grades. When we used all genes, pathway activation or suppression was not obvious, which shows the effectiveness of our algorithm. Our TF-centric pathway activation/suppression analysis technique is applicable to and useful for many other studies. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Pheotype specific subnetwork mining problem 5 2.1 Biological network construction methods 5 2.2 Necessity of biological sub-network mining algorithm 6 2.3 Problem formulation 6 2.4 Our information theoretic algorithm 7 Chapter 3 Method 10 3.1 TF-TG network construction 10 3.1.1 Edge set 10 3.1.2 Multi valued attribute vector 11 3.2 Information scores for TF-modules 11 3.2.1 Definition of TF-module 11 3.2.2 Entropy for TF-module 12 3.2.3 Best entropy with dynamic programming 13 3.2.4 Information score for TF-module 13 3.3 TF-module hyper graph 14 3.4 Merging of TF-modules on hyper-graph 15 Chapter 4 Result and Discussion 16 4.1 Raw biological data 16 4.2 HCS mining algorithm 16 4.3 TF-centric sub-network mining algorithm 17 4.4 Cell cycle: M-phase 20 4.5 Cell adhesion molecules 21 Chapter 5 Conclusion 24 Bibliography 27 요약 33 | - |
dc.format | application/pdf | - |
dc.format.extent | 793853 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | network mining | - |
dc.subject | subnetwork | - |
dc.subject.ddc | 621 | - |
dc.title | An Information Theoretic Algorithm for Mining and Ranking Phenotype-specific Sub-networks from Multi-class Gene Expression Data | - |
dc.title.alternative | 다중 클래스 유전자 발현 데이터에서 표현형 특이적 서브 네트워크 발굴 및 랭킹을 위한 정보 이론 기반 알고리즘 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | 35 | - |
dc.contributor.affiliation | 공과대학 전기·컴퓨터공학부 | - |
dc.date.awarded | 2017-02 | - |
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