A literature network of human genes for high-throughput analysis of gene expression

10 19,2001

Speaker: Shih-Te Yang

Advisor: Ueng-Cheng Yang

Summary

     There are many well jobs in using the statistical methods to analyze microarray nowadays. After clustering and selecting the significant genes expression profiles, a large number of genes need to incorporate the background knowledge. The biological information is existed in the scientific texts or journals, so it should be extracted automatically to interpret the high-throughput gene expression data. To link 1,3712 named human genes associations from public domain literature databases was implemented by gene-gene co-citation and constructed the network view in the PubGene^TM database. The content of this database was verified by manually curated DIP and OMIM. The relationship between genes was not extracted from Medline records, but the terms from the gene ontology (GO) and the medical subject heading (MeSH) were applied to annotate the genes. All of above have been created web-base tools to help analyze gene expression data. Moreover, this paper mentions two examples to describe how to link microarray data to PubGene^TM. The examples include two major types of microarray experimental design, discrimination and kinetic mechanism study to interpret expression data by biological point of view.

Reference cited

1.      Jenssen, T. K., Laegreid, A., Komorowski, J., Hovig, E., (2001). A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics.  97, 21-28.

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