Literature DB >> 15360918

Microarray data mining using gene ontology.

S Li1, M J Becich, J Gilbertson.   

Abstract

DNA microarray technology allows scientists to study the expression of thousands of genes--potentially entire genomes--simultaneously. However the large number of genes, variety of statistical methods employed and the complexity of biologic systems complicate analysis of microarray results. We have developed a web based environment that simplifies the presentation of microarray results by combining microarray results processed for statistical significance with probe set annotation by Genbank, NCBI RefSeqs, GeneCards and the Gene Ontology. This allows rapid examination and classification of microarray experiments--annotated by NCIBI tools --by Statistical Significance and Gene Oncology Classes. By providing a simple, easily understood interface to large microarray data sets, this tool has been particularly useful for small research groups focused on a small number of related genes and for researchers who want to ask simple questions without the overhead of complex data management and analysis.

Mesh:

Year:  2004        PMID: 15360918

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  9 in total

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2.  NanoParticle Ontology for cancer nanotechnology research.

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7.  A novel cross-disciplinary multi-institute approach to translational cancer research: lessons learned from Pennsylvania Cancer Alliance Bioinformatics Consortium (PCABC).

Authors:  Ashokkumar A Patel; John R Gilbertson; Louise C Showe; Jack W London; Eric Ross; Michael F Ochs; Joseph Carver; Andrea Lazarus; Anil V Parwani; Rajiv Dhir; J Robert Beck; Michael Liebman; Fernando U Garcia; Jeff Prichard; Myra Wilkerson; Ronald B Herberman; Michael J Becich
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8.  Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer.

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9.  Representing virus-host interactions and other multi-organism processes in the Gene Ontology.

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  9 in total

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