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Knowledge Discovery in Metabolic Pathways
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- 발행기관
- 보안공학연구지원센터(IJBSBT) 바로가기
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- 간행물
- International Journal of Bio-Science and Bio-Technology SCOPUS 바로가기
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- 통권
- Vol.5 No.3 (2013.06)바로가기
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- 페이지
- pp.11-28
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A207165
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Graph mining is a dynamic and active research area. In recent years, there is a remarkable boost in graph-structured data resulting graph mining a serious topic in research community. Graph clustering is the process of identifying similar structures in a large set of graphs. Graph clustering is also known as graph partitioning or grouping. This problem plays an important role in various data mining applications. Traditional approaches are centric towards optimization of graph clustering objectives such as ratio association or normalized cut. Spectral methods are also introduced which required Eigen-Vector computation. However these techniques are slow. We have presented a novel algorithm for detecting closely related groups of graph structures in KEGG metabolic pathways. The technique is based on structural similarity of connected fragments in graph-structured data. The technique is scalable to directed as well as undirected graphs. Preliminary experiments with synthesized data collected from KEGG were performed and their results are reported. The second contribution of this study is the modeling and analysis of combined metabolic reaction networks and relation network and showing their behavior towards scale free network.
목차
Abstract
1. Introduction
2. Graph Theory Preliminary
3. Graph Modeling for Metabolic Pathways
4. Clustering graph-structured Data
4.1. Step 1: Data Assignment
4.2. Step 2: Relocation of “Means”
5. Literature Review
6. gMean: Proposed Framework
6.1. Step-1 Training Graphs Data
6.2. Step 2: Graph Modeling
6.3. Step-3: Clustering
6.4. Step-4: Pruning
6.5. Step-5: Results
7. Results and Discussion
8. Conclusion
References
키워드
Graph clustering Biological metabolic pathways Enzyme Substrate Relation network Reaction network Scale free Network Cluster coefficient저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJBSBT) [Science & Engineering Research Support Center, Republic of Korea(IJBSBT)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Bio-Science and Bio-Technology
- 간기
- 격월간
- pISSN
- 2233-7849
- 수록기간
- 2009~2016
- 등재여부
- SCOPUS
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Bio-Science and Bio-Technology Vol.5 No.3
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