Feature construction can improve diagnostic criteria for high-dimensional metabolic data in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency

Clinical Chemistry

Volumn 53, Issue 7, 2007, Pages 1330-1337

  Ho, Sirikit ^a   Lukacs, Zoltan ^b   Hoffmann, Georg F ^a   Lindner, Martin ^a   Wetter, Thomas ^c  

^a UNIVERSITY HOSPITAL HEIDELBERG   (Germany)

^b UNIVERSITY MEDICAL CENTER HAMBURG EPPENDORF   (Germany)

^c UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MEDIUM CHAIN ACYL COENZYME A DEHYDROGENASE;

AMINOACIDURIA; ARTICLE; DATA ANALYSIS; DIAGNOSTIC TEST; ENZYME DEFICIENCY; FATTY ACID OXIDATION; HUMAN; MAJOR CLINICAL STUDY; NEWBORN; NEWBORN DISEASE; NEWBORN SCREENING; SCORING SYSTEM; STATISTICAL ANALYSIS;

ACYL-COA DEHYDROGENASE; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; CARNITINE; HUMANS; INFANT, NEWBORN; METABOLISM, INBORN ERRORS; NEONATAL SCREENING; SPECTROMETRY, MASS, ELECTROSPRAY IONIZATION; TANDEM MASS SPECTROMETRY;

EID: 34347390089     PISSN: 00099147     EISSN: None     Source Type: Journal    
DOI: 10.1373/clinchem.2006.081802     Document Type: Article

References (17)

1. Chace DH, Hillman SL, van Hove JLK, Naylor EW. Rapid diagnosis of MCAD deficiency: quantitative analysis of octanoylcarnitine and other acylcarnitines in newborn blood spots by tandem mass spectrometry. Clin Chem 1997;43:2106-13.
2. Clayton PT, Doig M, Ghafari S, Meaney C, Taylor C, Leonard JV, et al. Screening for medium chain acyl-CoA dehydrogenase deficiency using electrospray ionisation tandem mass spectrometry. Arch Dis Child 1998;79:109-15.
3. Pourfarzam M, Morris A, Appleton M, Craft A, Bartlett K. Neonatal screening for medium-chain acyl-CoA dehydrogenase deficiency. Lancet 2001;358:1063-4.
4. Okun JG, Kölker S, Schulze A, Kohlmüller D, Olgemöller K, Lindner M, et al. A method for quantitative acylcarnitine profiling in human skin fibroblasts using unlabelled palmitic acid: diagnosis of fatty oxidation disorders and differentiation between biochemical phenotypes of MCAD deficiency. Biochim Biophys Acta 2002;1584:91-8.
5. Van Hove JL, Zhang W, Kahler SG, Roe CR, Chen YT, Terada N, et al. Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency: diagnosis by acylcarnitine analysis in blood. Am J Hum Genet 1993;52:958-66.
6. Freitas AA. Understanding the crucial role of attribute interaction in data mining. Artif Intell Rev 2001;16:177-99.
7. Hu YJ, Kibler D. Generation of attributes for learning algorithms. Proceedings of the 13th National Conference on Artificial Intelligence, 1996;806-11.
8. Markovitch S, Rosenstein D. Feature generation using general constructor functions. Mach Learn 2002;49:59-98.
9. Baumgartner C, Baumgartner D. Biomarker discovery, disease classification, and similarity query processing on high-throughput MS/MS data of inborn errors of metabolism. J Biomol Screen 2006;11:90-9.
10. Baumgartner C, Böhm C, Baumgartner D. Modelling of classification rules on metabolic patterns including machine learning and expert knowledge. J Biomed Inform 2005;38:89-98.
11. Chace DH, Sherwin JE, Hillman SL, Lorey F, Cunningham GC. Use of phenylalanine-to-tyrosine ratio determined by tandem mass spectrometry to improve newborn screening for phenylketonuria of early discharge specimen collected in the first 24 hours. Clin Chem 1998;44:2405-9.
12. Johns Hopkins University, Baltimore, MD. Online Mendelian Inheritance in Man. MIM No. 201450: 2006-08-25. http://www.ncbi.nlm.nih.gov/omim/ (accessed April 24, 2007).
13. Schulze A, Lindner M, Kohlmüller D, Olgemöller K, Mayatepek E, Hoffmann GF. Expanded newborn screening for inborn errors of metabolism by electrospray ionization-tandem mass spectrometry: results, outcome and implications. Pediatrics 2003;111:1399-406.
14. Interdisziplinäre Screeningkommission der Deutschen Gesellschaft für Kinderheilkunde und Jugendmedizin: Richtlinien zur Organisation und Durchführung des Neugeborenenscreenings auf angeborene Stoffwechselstö rungen und Endokrinopathien in Deutschland. Monatsschr Kinderheilkd 2000;1424-40.
15. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995;1137-43.
16. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco: Morgan Kaufmann, 2005:525pp.
17. Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med 2000;19:1141-64.