Literature DB >> 17570735

DGEM--a microarray gene expression database for primary human disease tissues.

Yuni Xia1, Andrew Campen, Dan Rigsby, Ying Guo, Xingdong Feng, Eric W Su, Mathew Palakal, Shuyu Li.   

Abstract

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.

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Year:  2007        PMID: 17570735     DOI: 10.1007/BF03256235

Source DB:  PubMed          Journal:  Mol Diagn Ther        ISSN: 1177-1062            Impact factor:   4.074


  12 in total

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Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

10.  NASCArrays: a repository for microarray data generated by NASC's transcriptomics service.

Authors:  David J Craigon; Nick James; John Okyere; Janet Higgins; Joan Jotham; Sean May
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

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  1 in total

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