99 related articles for article (PubMed ID: 15180934)
1. Supervised machine learning techniques for the classification of metabolic disorders in newborns.
Baumgartner C; Böhm C; Baumgartner D; Marini G; Weinberger K; Olgemöller B; Liebl B; Roscher AA
Bioinformatics; 2004 Nov; 20(17):2985-96. PubMed ID: 15180934
[TBL] [Abstract][Full Text] [Related]
2. Modelling of classification rules on metabolic patterns including machine learning and expert knowledge.
Baumgartner C; Böhm C; Baumgartner D
J Biomed Inform; 2005 Apr; 38(2):89-98. PubMed ID: 15796999
[TBL] [Abstract][Full Text] [Related]
3. Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data.
Van den Bulcke T; Vanden Broucke P; Van Hoof V; Wouters K; Vanden Broucke S; Smits G; Smits E; Proesmans S; Van Genechten T; Eyskens F
J Biomed Inform; 2011 Apr; 44(2):319-25. PubMed ID: 21167313
[TBL] [Abstract][Full Text] [Related]
4. Feature construction can improve diagnostic criteria for high-dimensional metabolic data in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency.
Ho S; Lukacs Z; Hoffmann GF; Lindner M; Wetter T
Clin Chem; 2007 Jul; 53(7):1330-7. PubMed ID: 17513288
[TBL] [Abstract][Full Text] [Related]
5. Biomarker discovery, disease classification, and similarity query processing on high-throughput MS/MS data of inborn errors of metabolism.
Baumgartner C; Baumgartner D
J Biomol Screen; 2006 Feb; 11(1):90-9. PubMed ID: 16314408
[TBL] [Abstract][Full Text] [Related]
6. [Tandem mass spectrometry as screening for inborn errors of metabolism].
Campos H D
Rev Med Chil; 2011 Oct; 139(10):1356-64. PubMed ID: 22286738
[TBL] [Abstract][Full Text] [Related]
7. Population spectrum of ACADM genotypes correlated to biochemical phenotypes in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency.
Maier EM; Liebl B; Röschinger W; Nennstiel-Ratzel U; Fingerhut R; Olgemöller B; Busch U; Krone N; v Kries R; Roscher AA
Hum Mutat; 2005 May; 25(5):443-52. PubMed ID: 15832312
[TBL] [Abstract][Full Text] [Related]
8. Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines.
Georgoulas G; Stylios CD; Groumpos PP
IEEE Trans Biomed Eng; 2006 May; 53(5):875-84. PubMed ID: 16686410
[TBL] [Abstract][Full Text] [Related]
9. The failure to diagnose inborn errors of metabolism in New Zealand: the case for expanded newborn screening.
Wilson C; Kerruish NJ; Wilcken B; Wiltshire E; Webster D
N Z Med J; 2007 Sep; 120(1262):U2727. PubMed ID: 17891215
[TBL] [Abstract][Full Text] [Related]
10. [Screening of newborns for inborn errors of metabolism by tandem mass spectrometry].
Simonsen H
Ugeskr Laeger; 2002 Nov; 164(48):5607-12. PubMed ID: 12523003
[TBL] [Abstract][Full Text] [Related]
11. Screening of newborns and high-risk group of children for inborn metabolic disorders using tandem mass spectrometry in South Korea: a three-year report.
Yoon HR; Lee KR; Kang S; Lee DH; Yoo HW; Min WK; Cho DH; Shin SM; Kim J; Song J; Yoon HJ; Seo S; Hahn SH
Clin Chim Acta; 2005 Apr; 354(1-2):167-80. PubMed ID: 15748614
[TBL] [Abstract][Full Text] [Related]
12. Screening newborns for inborn errors of metabolism by tandem mass spectrometry.
Wilcken B; Wiley V; Hammond J; Carpenter K
N Engl J Med; 2003 Jun; 348(23):2304-12. PubMed ID: 12788994
[TBL] [Abstract][Full Text] [Related]
13. Expanded newborn screening: outcome in screened and unscreened patients at age 6 years.
Wilcken B; Haas M; Joy P; Wiley V; Bowling F; Carpenter K; Christodoulou J; Cowley D; Ellaway C; Fletcher J; Kirk EP; Lewis B; McGill J; Peters H; Pitt J; Ranieri E; Yaplito-Lee J; Boneh A
Pediatrics; 2009 Aug; 124(2):e241-8. PubMed ID: 19620191
[TBL] [Abstract][Full Text] [Related]
14. Neonatal screening for medium chain acyl-CoA deficiency: high incidence in Lower Saxony (northern Germany).
Sander S; Janzen N; Janetzky B; Scholl S; Steuerwald U; Schäfer J; Sander J
Eur J Pediatr; 2001 May; 160(5):318-9. PubMed ID: 11388605
[No Abstract] [Full Text] [Related]
15. Clinical and laboratory approach to a neonate suspected of an inborn error of metabolism.
Kang ES
Turk J Pediatr; 1999; 41(1):1-35. PubMed ID: 10770673
[No Abstract] [Full Text] [Related]
16. Cancer classification and prediction using logistic regression with Bayesian gene selection.
Zhou X; Liu KY; Wong ST
J Biomed Inform; 2004 Aug; 37(4):249-59. PubMed ID: 15465478
[TBL] [Abstract][Full Text] [Related]
17. Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.
Chen WH; Hsieh SL; Hsu KP; Chen HP; Su XY; Tseng YJ; Chien YH; Hwu WL; Lai F
J Med Internet Res; 2013 May; 15(5):e98. PubMed ID: 23702487
[TBL] [Abstract][Full Text] [Related]
18. Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.
Gamberger D; Lavrac N; Zelezný F; Tolar J
J Biomed Inform; 2004 Aug; 37(4):269-84. PubMed ID: 15465480
[TBL] [Abstract][Full Text] [Related]
19. Policy issues related to expanded newborn screening: a review of three genetic/metabolic disorders.
Bishop Hubbard H
Policy Polit Nurs Pract; 2007 Aug; 8(3):201-9. PubMed ID: 18178927
[TBL] [Abstract][Full Text] [Related]
20. Newborn screening--is it really that simple?
Wiley V; Carpenter K; Bayliss U; Wilcken B
Southeast Asian J Trop Med Public Health; 2003; 34 Suppl 3():107-10. PubMed ID: 15906711
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]