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KMID : 1034820050010040281
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Molecular & Cellular Toxicology
2005 Volume.1 No. 4 p.281 ~ p.283
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BINGO: Biological Interpretation Through Statistically and Graph-theoretically Navigating Gene
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Lee Sung-Geun
Yang Jae-Seong
Chung Il-Kyung
Kim Yang-Seok
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Abstract
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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.
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KEYWORD
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Bioinformatics, Toxicoinformatics, Data mining
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