title: Neural network predicts sequence of TP53 gene based on DNA chip.
abstract: We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence.
  
  
  BACKGROUND
  The trained neural network is available for academic use by contacting steen@cbs.dtu.dk
authors.affiliation: Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.
authors.forename: Jeppe S
authors.initials: JS
authors.lastname: Spicker
authors.forename: Friedrik
authors.initials: F
authors.lastname: Wikman
authors.forename: Ming-Lan
authors.initials: ML
authors.lastname: Lu
authors.forename: Carlos
authors.initials: C
authors.lastname: Cordon-Cardo
authors.forename: Christopher
authors.initials: C
authors.lastname: Workman
authors.forename: Torben F
authors.initials: TF
authors.lastname: ØRntoft
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
authors.forename: Steen
authors.initials: S
authors.lastname: Knudsen
mesh_terms: D001483:Base Sequence; D016158:Genes, p53 / Q000235:genetics*; D006801:Humans; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*