| Info. | Vol.5 - No.1 (2011.03.20) |
|---|---|
| Title | Semantic data integration to biological relationship among chemicals, diseases, and differential expressed genes |
| Authors | Byeong-Chul Kang1, Hyung-Yong Kim1, Ga-Hee Shin1, Kyoo-Yeol Lee1, Yeon-Kyung Kang1, Seung-Jun Kim2,3, Yu-Ri An2, Seung-Yong Hwang3,4 & Jae-Chun Ryu5 |
| Institutions | 1Insilicogen Inc., Gweonseon-gu, Suwon, Gyeonggi-do, Korea 2Department of Biochemistry, Hanyang University, Sangnok-gu, Ansan, Gyeonggi-do, Korea 3GenoGheck Co. Ltd., Sangnok-gu, Ansan, Gyeonggi-do, Korea 4Department of Bio-Nanotechnology, Hanyang University, Snagnok-gu, Ansan, Gyeonggi-do, Korea 5Laboratory of Cellular and Molecular Toxicology, Korea Institute of Science and Technology, Seoul, Korea Correspondence and requests for materials should be addressed to B.-C. Kang ( bckang@insilicogen.com) |
| Abstract | Systems approaches are showing early promise in helping bridge the gap between pathophys-iological processes and their molecular determinants. In toxicology, microarray technology leads rapid screening of DEGs (differential expressed gene) from various kinds of chemical exposes. Using toxicogenomics for the risk assessment, various and heterogeneous data are contributed to each step, such as genome sequence, genotype, gene expression, phenotype, disease information, etc. To derive actual roles of the DEGs, it is essentially required to construct interactions among DEGs and to link the known information of diseases. Proper data model is essential and critical component to build information system for risk assessment. Our study suggests a semantic modeling strategy to organize heterogeneous data types and introduces techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and infor- mation layers) that are used to represent complex bio-medical networks. We depict reconstruction of seman-tic relationship among chemicals, diseases, and DEGs in public available data. In this work, user?셲 experiment results can be easily uploaded and bound to the current data network. This feature provides to maintain user?셲 specific interactions from their interesting DEGs to publicly available disease and chemical data. The program was built upon DjangoWeb framework in Python language and commercial text-mining engine, MedScan, was employed. Example analysis was completed for evaluation of the system and presented in this paper. We are expecting that this work provides rapid way to build custom-driven toxico- knowledge-base by integrating customers internal documents and public data. |
| Keyword | Text-mining, Data-mining, Microarray, Gene expression, Semantics |
| PDF File |
 # 2010년도 발행분 부터는 Springer 의 BioChip Journal 페이지에서 전문을 열람하실 수 있습니다.
# 학회회원 로그인 후 [ Springer BioChip Journal 열람하기 ] 버튼을 클릭하시면 새창으로 열립니다. |
