Supervised machine learning techniques for the classification of metabolic disorders in newborns

Bioinformatics

Volumn 20, Issue 17, 2004, Pages 2985-2996

  Baumgartner, Christian ^a   Bohm C ^b   Baumgartner, D ^c   Marini, G ^d   Weinberger, K ^d   Olgemoller B ^e   Liebl, B ^f   Roscher, A A ^g  

^a UNIVERSITY FOR HEALTH SCIENCES   (Austria)

^b UNIVERSITY OF MUNICH   (Germany)

^c INNSBRUCK MEDICAL UNIVERSITY   (Austria)

^d BIOCRATES LIFE SCIENCES AG   (Austria)

^e Labor Becker   (Germany)

^f Landesuntersuchungsamt für amt Gesundheitswesen Siidbayern   (Germany)

^g LUDWIG MAXIMILIANS UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MEDIUM CHAIN ACYL COENZYME A DEHYDROGENASE; PHENYLALANINE;

ACCURACY; AMINO ACID METABOLISM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DECISION THEORY; DISCRIMINANT ANALYSIS; DISEASE CLASSIFICATION; DISEASE MARKER; ENZYME DEFICIENCY; FATTY ACID METABOLISM; HUMAN; INBORN ERROR OF METABOLISM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING; LOGISTIC REGRESSION ANALYSIS; NEWBORN; NEWBORN SCREENING; PHENYLKETONURIA; PREDICTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; TANDEM MASS SPECTROMETRY;

EID: 10244245449     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bth343     Document Type: Article

References (33)

1. American College of Medical Genetics/American Society of Human Genetics Test and Technology Transfer Committee Working Group (2000) Tandem mass spectrometry in newborn screening. Genet. Med., 2, 267-269.
2. Baumgartner,C., Baumgartner,D. and Böhm, C. (2004) Classification on high dimensional metabolic data: phenylketonuria as an example. IASTED Proceedings of 2nd International Conference on Biomedical Engineering (BioMED 2004), Innsbruck, Austria, pp. 357-360.
3. Bishop,C.M. (1995) Neural Networks for Pattern Recognition Oxford University Press, Oxford.
4. Blau,N., Thony,B., Cotton,R.G.H. and Hyland,K. (2001) Disorders of tetrahydrobiopterin and related biogenic amines. In Scriver,C.R., Kaufman,S., Eisensmith,E., Woo,S.L.C., Vogelstein,B. and Childs,B. (eds), The Metabolic and Molecular Bases of Inherited Disease, 8th edn. McGraw Hill, New York, pp. 1725-1776.
5. Chace,D.H., Millington,D.S., Terada,N., Kahler,S.G., Roe,C.R. and Hofman,L.F. (1993) Rapid diagnosis of phenylketonuria by quantitative analysis for phenylalanine and tyrosine in neonatal blood spots by tandem mass spectrometry. Clin. Chem., 39, 66-71.
6. Chace,D.H., Sherwin,J.E., Hillman,S.L., Lorey,F. and Cunningham,G.C. (1998) Use of phenylalanine-to-tyrosine ratio determined by tandem mass spectrometry to improve newborn screening for phenylketonuria of early discharge specimens collected in the first 24 hours. Clin. Chem., 44, 2405-2409.
7. Chace,D.H., DiPerna,J.C. and Naylor,E.W. (1999) Laboratory integration and utilization of tandem mass spectrometry in neonatal screening: a model for clinical mass spectrometry in the next millennium. Acta Paediatr. Suppl., 88, 45-47.
8. Cortes,C. and Vapnik,V. (1995) Support vector networks. Mach. Learning, 20, 273-297.
9. Dreiseitl,S., Ohno-Machado,L., Kittler,H., Vinterbo,S., Billhardt,H. and Binder,M. (2001) A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J. Biomed. Inform., 34, 28-36.
10. Guldberg,P., Rey,F., Zschocke,J., Romano,V., Francois,B., Michiels,L., Ullrich,K., Hoffmann,G.F., Burgard,P., Schmidt,H. et al. (1998) A European multicenter study of phenylalanine hydroxylase deficiency: classification of 105 mutations and a general system for genotype-based prediction of metabolic phenotype. Am. J. Hum. Genet. 63, 71-79
11. Hall,M.A. (1999) Correlation-based feature selection for machine learning. PhD Thesis. University of Waikato, New Zealand.
12. Hosmer,D.W. and Lemeshow,S. (2000) Applied Logistic Regression, 2nd edn. Wiley, New York.
13. Kira,K. and Rendell,L.A. (1992) A practical approach to feature selection. In Machine Learning: Proceedings of the Ninth International Conference, Aberdeen, Scotland, pp. 249-256.
14. Kononenko,I. (1995) On biases in estimating multi-valued attributes. In IJCAI'95, Montreal, Canada, pp. 1034-1040.
15. Liebl,B., Nennstiel-Ratzel,U., von Kries,R., Fingerhut,R., Olgemoller,B., Zapf,A. and Roscher,A.A. (2002a) Very high compliance in an expanded MS-MS-based newborn screening program despite written parental consent. Prev. Med., 34, 127-131.
16. Liebl,B., Nennstiel-Ratzel,U., von Kries,R., Fingerhut,R., Olgemoller,B., Zapf,A. and Roscher,A.A. (2002b) Expanded newborn screening in Bavaria: tracking to achieve requested repeat testing. Prev. Med., 34, 132-137.
17. Liebl,B., Nennstiel-Ratzel,U., Roscher,A.A. and von Kries,R. (2003) Data required for the evaluation of newborn screening programmes. Eur. J. Pediatr., 162(Suppl. 1), 57-61.
18. McLachlan,G.J. (1992) Discriminant Analysis and Statistical Pattern Recognition. Wiley, New York.
19. Mendes,P. (2002) Emerging bioinformatics for the metabolome. Brief. Bioinform., 3, 134-145.
20. Millington,D.S., Roe,C.R. and Maltby,D.A. (1984) Application of high resolution fast atom bombardment and constant B/E ratio linked scanning to the identification and analysis of acylcarnitines in metabolic disease. Biomed. Mass Spectrom., 11, 236-241.
21. Millington,D.S., Terada,N., Kodo,K. and Chace,D.H. (1992) A review: carnitine and acylcarnitine analysis in the diagnosis of metabolic diseases: advantages of tandem mass spectrometry. In Matsumoto,I. (ed). Advances in Chemical Diagnosis and Treatment of Metabolic Disorders. John Wiley & Sons, New York, Vol 1, pp. 59-71.
22. Mitchell,T.M. (1997) Machine Learning. McGraw-Hill, Boston, MA.
23. National Center for Biotechnology Information. Online Mendelian Inheritance in Man (OMIM), http://www3.ncbi.nlm.nih.gov/Omin
24. Neville,P., Tan,P.Y., Mann,G. and Wolfinger,R. (2003) Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum. Proteomics, 3, 1710-1715.
25. Purohit,P.V. and Rocke,D.M. (2003) Discriminant models for high-throughput proteomics mass spectrometer data. Proteomics, 3 1699-1703.
26. Quinlan,R.J. (1986) Induction of decision trees. Mach. Learning, 1, 81-106.
27. Quinlan,R.J. (1993) C4.5: Program for Machine Learning. Morgan Kaufmann, San Mateo, CA.
28. Rashed,M.S., Ozand,P.T., Bucknall,M.P. and Little,D. (1995) Diagnosis of inborn errors of metabolism from blood spots by acylcarnitines and amino acids profiling using automated electrospray tandem mass spectrometry. Pediatr. Res., 38, 324-331.
29. Rinaldo,P., Matern,D. and Bennett,M.J. (2002) Fatty acid oxidation disorders. Annu. Rev. Physiol., 64, 477-502.
30. Salzberg,S. (1999) On comparing classifiers: a critique of current research and methods. Data Min. Knowl. Disc., 1, 1-12.
31. Van Hove,J.L., Zhang,W., Kahler,S.G., Roe,C.R., Chen,Y.T., Terada,N., Chace,D.H., Iafolla,A.K., Ding,J.H., and Millington,D.S. (1993) Medium-chain acyl-CoA dehydrogenase (MCAD) deficiency: diagnosis by acylcarnitine analysis in blood. Am. J. Hum. Genet., 52, 958-966.
32. Vapnik,V. (1998) Statistical Learning Theory. Wiley, New York.
33. Witten,I.H. and Frank,E. (2000) Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA.