한국생물정보학회:학술대회논문집 (Proceedings of the Korean Society for Bioinformatics Conference)
- 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
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- Pages.124-130
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- 2004
Determining differentially expressed genes in a microarray expression dataset based on the global connectivity structure of pathway information
- Chung, Tae-Su (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Human Genome Research Institute, Seoul National University College of Medicine) ;
- Kim, Kee-Won (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine) ;
- Lee, Hye-Won (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine) ;
- Kim, Ju-Han (Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Human Genome Research Institute, Seoul National University College of Medicine)
- 발행 : 2004.11.04
초록
Microarray expression datasets are incessantly cumulated with the aid of recent technological advances. One of the first steps for analyzing these data under various experimental conditions is determining differentially expressed genes (DEGs) in each condition. Reasonable choices of thresholds for determining differentially expressed genes are used for the next -step-analysis with suitable statistical significances. We present a model for identifying DEGs using pathway information based on the global connectivity structure. Pathway information can be regarded as a collection of biological knowledge, thus we are tying to determine the optimal threshold so that the consequential connectivity structure can be the most compatible with the existing pathway information. The significant feature of our model is that it uses established knowledge as a reference to determine the direction of analyzing microarray dataset. In the most of previous work, only intrinsic information in the miroarray is used for the identifying DEGs. We hope that our proposed method could contribute to construct biologically meaningful network structure from microarray datasets.
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