mRNA-centric semantic modeling for finding molecular signature of trace chemical in human blood

• Published in: Molecular & cellular toxicology Vol. 8; no. 1; pp. 35 - 41
• Main Authors: Shin, Ga-Hee, Kang, Yeon-Kyung, Lee, Seung-Hun, Kim, Seung Jun, Hwang, Seung Yong, Nam, Suk-Woo, Ryu, Jae-Chun, Kang, Byeong-Chul
• Format: Journal Article
• Language: English
• Published: Heidelberg The Korean Society of Toxicogenomics and Toxicoproteomics 01.03.2012; 대한독성 유전단백체 학회
• Subjects: Biomedical and Life Sciences; Cell Biology; Life Sciences; Original Paper; Pharmacology/Toxicology; 생물학; Data-mining; Gene expression; Microarray; Semantic modeling
• ISSN: 1738-642X; 2092-8467
• DOI: 10.1007/s13273-012-0005-9

The vast quantities of information on toxicogenomics such as genome sequence, genotype, gene expression, phenotype, disease information, etc. are reflected in scientific literature. However, these various and heterogeneous data has to be reconstructed by proper data model to enhance our understanding. This study suggests a semantic modeling to organize heterogeneous data types and introduces techniques and concepts such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers. These concepts were used to represent complex biochemical networks relationship. The semantic modeling tool is used as an example to demonstrate how a domain such as risk assessment is represented and how this representation is utilized for research. In this work, we show focusing on mRNA centric semantic model as a representative. From experimental data, text-mining results and public databases we generate mRNA-centric semantic modeling and demonstrate its use by mining specific molecular networks together. Application of semantic modeling: 1. Common DEGs which are differentially expressed mRNA by VOCs are identified. 2. Diseases and biological processes associated with common DEGs are identified. 3. Finally subjects associated with common disease, biological process and DEGs are identified.