Quality Assessment of MAGE-ML Genomic Datasets Using DescribeX
Identifieur interne : 000421 ( Main/Exploration ); précédent : 000420; suivant : 000422Quality Assessment of MAGE-ML Genomic Datasets Using DescribeX
Auteurs : Lorena Etcheverry [Uruguay] ; Shahan Khatchadourian [Canada, États-Unis] ; Mariano Consens [Canada, États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: The functional genomics and informatics community has made extensive microarray experimental data available online, facilitating independent evaluation of experiment conclusions and enabling researchers to access and reuse a growing body of gene expression knowledge. While there are several data-exchange standards, numerous microarray experiment datasets are published using the MAGE-ML XML schema. Assessing the quality of published experiments is a challenging task, and there is no consensus among microarray users on a framework to measure dataset quality. In this paper, we develop techniques based on DescribeX (a summary-based visualization tool for XML) that quantitatively and qualitatively analyze MAGE-ML public collections, gaining insights about schema usage. We address specific questions such as detection of common instance patterns and coverage, precision of the experiment descriptions, and usage of controlled vocabularies. Our case study shows that DescribeX is a useful tool for the evaluation of microarray experiment data quality that enhances the understanding of the instance-level structure of MAGE-ML datasets.
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DOI: 10.1007/978-3-642-15120-0_15
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While there are several data-exchange standards, numerous microarray experiment datasets are published using the MAGE-ML XML schema. Assessing the quality of published experiments is a challenging task, and there is no consensus among microarray users on a framework to measure dataset quality. In this paper, we develop techniques based on DescribeX (a summary-based visualization tool for XML) that quantitatively and qualitatively analyze MAGE-ML public collections, gaining insights about schema usage. We address specific questions such as detection of common instance patterns and coverage, precision of the experiment descriptions, and usage of controlled vocabularies. 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