title: Unbiased pattern detection in microarray data series.
abstract: BACKGROUND
  Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many cases the aim is to identify pattern in the data series generated by successive microarray measurements.
  
  
  RESULTS
  Here we introduce a new method of detecting pattern in microarray data series which is independent of the nature of this pattern. Our approach provides a measure of the algorithmic compressibility of each data series. A series which is significantly compressible is much more likely to result from simple underlying mechanisms than series which are incompressible. Accordingly, the gene associated with a compressible series is more likely to be biologically significant. We test our method on microarray time series of yeast cell cycle and show that it blindly selects genes exhibiting the expected cyclic behaviour as well as detecting other forms of pattern. Our results successfully predict two independent non-microarray experimental studies.
authors.affiliation: Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK. sea31@cam.ac.uk
authors.forename: S E
authors.initials: SE
authors.lastname: Ahnert
authors.forename: K
authors.initials: K
authors.lastname: Willbrand
authors.forename: F C S
authors.initials: FC
authors.lastname: Brown
authors.forename: T M A
authors.initials: TM
authors.lastname: Fink
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005656:Fungal Proteins / Q000737:chemistry; D020869:Gene Expression Profiling / Q000379:methods; D005800:Genes, Fungal; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software
chemical_list: D005656:Fungal Proteins