• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.13-16
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• 2005

The current vision of toxicogenomics is the development of methods or platforms to predict toxicity of un characterized chemicals by using '-omics' information in pre-clinical stage. Because each chemical has different ADME (absorption, distribution, mechanism, excretion) and experimental animals have lots of variation, precise prediction of chemical's toxicity based on '-omics' information and toxicity data of known chemicals is very difficult problem. So, the importance of bioinformatics is more emphasized on toxicogenomics than other functional genomics studies because these problems can not be solved only with experiments. Thus, toxicoinformatics covers all information-based analytical methods from gene expression (bioinformatics) to chemical structures (cheminformatics) and it also deals with the integration of wide range of experimental data for further extensive analyses. In this review, the overall strategy to toxicoinformatics is discussed.

• Genomics & Informatics
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• v.2 no.4
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• pp.153-162
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• 2004

• Molecular & Cellular Toxicology
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• v.1 no.1
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• pp.1-6
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• 2005

Toxicology is a multidisciplinary field, and an important science that impacts both environmental health regulation and the development and practice of medicine. The rapid progress in cellular and molecular biology, like many other branches of biomedical research, toxicology is now experiencing a renaissance fueled by the application of "omic" technologies to gain a better understanding of the biological basis of toxicology of drugs and other environmental factors. In this review on current progress on toxicology, the future perspective, concept, approaches and applications of toxicogenomics as next generation promising technology in toxicology field will be described.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.