Automatic construction of gene relation networks using text mining and gene expression data

Medical Informatics and the Internet in Medicine
Thomas KaropkaAnne Glass

Abstract

Microarray gene expression analysis is a powerful high-throughput technique that enables researchers to monitor the expression of thousands of genes simultaneously. Using this methodology huge amounts of data are produced which have to be analysed. Clustering algorithms are used to group genes together based on a predefined distance measure. However, clustering algorithms do not necessarily group the genes in a biological meaningful way. Additional information is needed to improve the identification of disease relevant genes. The primary objective of our project is to support the analysis of microarray gene expression data by construction of gene relation networks (GRNs). Required information can not be found in a structured representation like a database. In contrast, a large number of relations are described in biomedical literature. The main outcome of this project is the implementation of a software system that provides clinicians and researchers with a tool that supports the analysis of microarray gene expression data by mapping known relationships from the biomedical literature to local gene expression experiments.

References

Apr 1, 1997·Trends in Genetics : TIG·M RebhanD Lancet
Dec 9, 1998·Proceedings of the National Academy of Sciences of the United States of America·M B EisenD Botstein
Mar 17, 1999·Proceedings of the National Academy of Sciences of the United States of America·P TamayoT R Golub
Dec 11, 1999·Nature·L H HartwellA W Murray
Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·N FriedmanD Pe'er
Jan 11, 2000·Nucleic Acids Research·G D BaderC W Hogue
Jun 5, 2001·Nature Reviews. Genetics·J Quackenbush
Sep 13, 2001·Proceedings of the National Academy of Sciences of the United States of America·T SørlieA L Børresen-Dale
Sep 19, 2001·Medical Informatics and the Internet in Medicine·S HoelzerJ Dudeck
Jan 11, 2002·Medical Informatics and the Internet in Medicine·M DugasK Uberla
Mar 26, 2002·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Hidde de Jong
Mar 26, 2002·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Edward R DoughertyJeffrey M Trent
Mar 26, 2002·FEBS Letters·Andreas ZanzoniGianni Cesareni

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Citations

May 1, 2005·Expert Review of Clinical Immunology·Vladimir Brusic, Nikolai Petrovsky
Apr 28, 2009·Journal of Biomedical Informatics·Anália LourençoMiguel Rocha
Nov 4, 2006·Journal of Biomedical Science·Mathew PalakalShielly Hartanto
Mar 23, 2019·Scientific Reports·Ziqiao YinZhiming Zheng
Sep 28, 2006·Pancreas·Anne GlassGisela Sparmann

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