Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional CodeIntegrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code
- Other Titles
- Integrative Analysis of Microarray Data with Gene Ontology to Select Perturbed Molecular Functions using Gene Ontology Functional Code
- Authors
- 김창식; 최지원; 윤석준
- Issue Date
- Jun-2009
- Publisher
- 한국유전체학회
- Keywords
- breast cancer; gene ontology code; gene ontology functional code; microarray; molecular functions
- Citation
- Genomics & Informatics, v.7, no.2, pp 122 - 130
- Pages
- 9
- Journal Title
- Genomics & Informatics
- Volume
- 7
- Number
- 2
- Start Page
- 122
- End Page
- 130
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14002
- ISSN
- 1598-866X
2234-0742
- Abstract
- A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology
Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the
root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions
could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed
molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.
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