Title: Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data
Authors: Van den Bulcke, Tim ×
Vanden Broucke, Paul
Van Hoof, Viviane
Wouters, Kristien
vanden Broucke, Seppe
Smits, Geert
Smits, Elke
Proesmans, Sam
Van Genechten, Toon
Eyskens, Francois #
Issue Date: Apr-2011
Publisher: Academic Press
Series Title: Journal of Biomedical Informatics vol:44 issue:2 pages:319-325
Abstract: Newborn screening programs for severe metabolic disorders using tandem mass spectrometry are widely used. Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid oxidation defect (1:15,000 newborns) and it has been proven that early detection of this metabolic disease decreases mortality and improves the outcome. In previous studies, data mining methods on derivatized tandem MS datasets have shown high classification accuracies. However, no machine learning methods currently have been applied to datasets based on non-derivatized screening methods.
ISSN: 1532-0464
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
data mining methods for classification of MCADD using non-derivatized tandem MS neonatal screening data - Copy.pdfMain article Published 641KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science