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Conference proceeding
Exploratory Analysis of Protein Translation Regulatory Networks Using Hierarchical Random Graphs
2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, pp 118-123
01 Jan 2009
Abstract
Protein translation is a vital cellular process for any living organism. The availability of interaction databases provides an opportunity for researchers to exploit the immense amount of data in silico such as studying biological networks. There has been an extensive effort using computational methods in deciphering the transcriptional regulatory networks. However, research on translation regulatory networks has caught little attention in the bioinformatics and computational biology community. In this paper, we present an exploratory analysis of yeast protein translation regulatory networks using hierarchical random graphs. We derive a protein translation regulatory network from a protein-protein interaction dataset. Using a hierarchical random graph model, we show that the network exhibits well organized hierarchical structure. In addition, we apply this technique to predict missing links in the network. The results have potential implications for better understanding mechanisms of translational control from a system's perspective.
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Details
- Title
- Exploratory Analysis of Protein Translation Regulatory Networks Using Hierarchical Random Graphs
- Creators
- Daniel Duanqing Wu - Drexel UniversityXiaohua Hu - Drexel UniversityTingting He - Central China Normal UniversityIEEE
- Publication Details
- 2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, pp 118-123
- Series
- IEEE International Conference on Bioinformatics and Biomedicine-BIBM
- Publisher
- IEEE
- Number of pages
- 3
- Grant note
- IIS 0448023 / NSF; National Science Foundation (NSF) B07042 / Programme of Introducing Talents of Discipline to Universities (China); Ministry of Education, China - 111 Project
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000275900200022
- Scopus ID
- 2-s2.0-74549205821
- Other Identifier
- 991019167663204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic