title: Machine learning techniques to identify putative genes involved in nitrogen catabolite repression in the yeast Saccharomyces cerevisiae.
abstract: BACKGROUND
  Nitrogen is an essential nutrient for all life forms. Like most unicellular organisms, the yeast Saccharomyces cerevisiae transports and catabolizes good nitrogen sources in preference to poor ones. Nitrogen catabolite repression (NCR) refers to this selection mechanism. All known nitrogen catabolite pathways are regulated by four regulators. The ultimate goal is to infer the complete nitrogen catabolite pathways. Bioinformatics approaches offer the possibility to identify putative NCR genes and to discard uninteresting genes.
  
  
  RESULTS
  We present a machine learning approach where the identification of putative NCR genes in the yeast Saccharomyces cerevisiae is formulated as a supervised two-class classification problem. Classifiers predict whether genes are NCR-sensitive or not from a large number of variables related to the GATA motif in the upstream non-coding sequences of the genes. The positive and negative training sets are composed of annotated NCR genes and manually-selected genes known to be insensitive to NCR, respectively. Different classifiers and variable selection methods are compared. We show that all classifiers make significant and biologically valid predictions by comparing these predictions to annotated and putative NCR genes, and by performing several negative controls. In particular, the inferred NCR genes significantly overlap with putative NCR genes identified in three genome-wide experimental and bioinformatics studies.
  
  
  CONCLUSIONS
  These results suggest that our approach can successfully identify potential NCR genes. Hence, the dimensionality of the problem of identifying all genes involved in NCR is drastically reduced.
authors.affiliation: Machine Learning Group, Département d'Informatique, Faculté des Sciences, Université Libre de Bruxelles (ULB), Boulevard du Triomphe CP 212, 1050 Brussels, Belgium. kkontos@ulb.ac.be
authors.forename: Kevin
authors.initials: K
authors.lastname: Kontos
authors.forename: Patrice
authors.initials: P
authors.lastname: Godard
authors.forename: Bruno
authors.initials: B
authors.lastname: André
authors.forename: Jacques
authors.initials: J
authors.lastname: van Helden
authors.forename: Gianluca
authors.initials: G
authors.lastname: Bontempi