title: Identifying genes from up-down properties of microarray expression series.
abstract: BACKGROUND
  We consider any collection of microarrays that can be ordered to form a progression; for example, as a function of time, severity of disease or dose of a stimulant. By plotting the expression level of each gene as a function of time, or severity, or dose, we form an expression series, or curve, for each gene. While most of these curves will exhibit random fluctuations, some will contain a pattern, and these are the genes that are most likely associated with the quantity used to order them.
  
  
  RESULTS
  We introduce a method of identifying the pattern and hence genes in microarray expression curves without knowing what kind of pattern to look for. Key to our approach is the sequence of ups and downs formed by pairs of consecutive data points in each curve. As a benchmark, we blindly identified genes from yeast cell cycles without selecting for periodic or any other anticipated behaviour.
  
  
  BACKGROUND
  tmf20@cam.ac.uk
  
  
  BACKGROUND
  The complete versions of Table 2 and Figure 4, as well as other material, can be found at http://www.lps.ens.fr/~willbran/up-down/ or http://www.tcm.phy.cam.ac.uk/~tmf20/up-down/
authors.affiliation: Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.
authors.forename: Karen
authors.initials: K
authors.lastname: Willbrand
authors.forename: Francois
authors.initials: F
authors.lastname: Radvanyi
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Nadal
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Thiery
authors.forename: Thomas M A
authors.initials: TM
authors.lastname: Fink
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors