File: stress_test_pubmed.xml

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<ArticleTitle>TESTING ARTICLE</ArticleTitle>
</Article>
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                <ArticleId IdType="medline">21583752</ArticleId>
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<Year>2001</Year>
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<Abstract>
<AbstractText>Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.</AbstractText>
</Abstract>
<Affiliation>European Bioinformatics Institute, EMBL outstation, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. brazma@ebi.ac.uk</Affiliation>
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</Author>
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<LastName>Kim</LastName>
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<MeshHeading>
<DescriptorName>Gene Expression Profiling</DescriptorName>
<QualifierName>methods</QualifierName>
</MeshHeading>
<MeshHeading>
<DescriptorName>Oligonucleotide Array Sequence Analysis</DescriptorName>
<QualifierName MajorTopicYN="Y">standards</QualifierName>
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<DateCreated>
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<Month>10</Month>
<Day>02</Day>
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<Year>2001</Year>
<Month>12</Month>
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<Issue>10</Issue>
<PubDate>
<Year>2001</Year>
<Month>Aug</Month>
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<ArticleTitle>Gene expression data analysis.</ArticleTitle>
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</Pagination>
<Abstract>
<AbstractText>Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices, tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification as well as some possible future directions.</AbstractText>
</Abstract>
<Affiliation>European Molecular Biology Laboratory, Outstation Hinxton--the European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, Cambridge, UK. brazma@ebi.ac.uk</Affiliation>
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<LastName>Brazma</LastName>
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<Country>France</Country>
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