Lorena|
Etcheverry|
lorenae@fing.edu.uy|
InCo|
Quality Assessment of MAGE-ML Genomic Datasets using DescribeX|
xml, scientific data quality|
MAESTRIA|
Trabajo en conjunto con Mariano Consens y Shahan Khatchadourian de la Universidad de Toronto|
MAGE-ML has been designed by the functional genomics community as a standard XML
exchange format for microarray experiment datasets. Quality assessment of published experiments is a challenging task since there is no consensus among the community of microarray users on a framework to measure its quality.

In this paper we describe how DescribeX (a summary-based visualization tool for XML) can quantitatively and qualitatively analyze MAGE-ML public collections and gain 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 data sources.|