Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles.

Prank, Klaus; Schulze, Egbert; Eckert, Olaf; Nattkemper, Tim W; Bettendorf, Markus; Maser-Gluth, Christiane; Sejnowski, Terrence J; Grote, Arno; Penner, Erika; von Zur Mühlen, Alexander; Brabant, Georg

Prank, Klaus; Schulze, Egbert; Eckert, Olaf; Nattkemper, Tim W; Bettendorf, Markus; Maser-Gluth, Christiane; Sejnowski, Terrence J; Grote, Arno; Penner, Erika; von Zur Mühlen, Alexander; Brabant, Georg.

Affiliation

Prank K; International NRW Graduate School in Bioinformatics and Genome Research Center of Biotechnology (CeBiTec), Bielefeld University, Germany. klaus.prank@cebitec.uni-bielefeld.de

Eur J Endocrinol ; 153(2): 301-5, 2005 Aug.

Article in En | MEDLINE | ID: mdl-16061837

Subject(s)

Artificial Intelligence; Chromosome Mapping/methods; Models, Genetic; Steroid 21-Hydroxylase/genetics; Steroids/blood; Adult; Genotype; Heterozygote; Humans; Linear Models; Middle Aged; Mutation; Nonlinear Dynamics; Phenotype; Predictive Value of Tests

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Steroids / Artificial Intelligence / Steroid 21-Hydroxylase / Chromosome Mapping / Models, Genetic Type of study: Prognostic_studies / Risk_factors_studies Limits: Adult / Humans / Middle aged Language: En Journal: Eur J Endocrinol Journal subject: ENDOCRINOLOGIA Year: 2005 Document type: Article Affiliation country: Germany

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