title: ProbeSelect: selecting differentially expressed probes in transcriptional profile data.
abstract: CONCLUSIONS
  Transcriptional profiling still remains one of the most popular techniques for identifying relevant biomarkers in patient samples. However, heterogeneity in the population leads to poor statistical evidence for selection of most relevant biomarkers to pursue. In particular, human transcriptional differences can be subtle, making it difficult to tease out real differentially expressed biomarkers from the variability inherent in the population. To address this issue, we propose a simple statistical technique that identifies differentially expressed probes in heterogeneous populations as compared with controls.
  
  
  METHODS
  The algorithm has been implemented in Java and available at www.sourceforge.net/projects/probeselect.
authors: Patient Stratification group and Genetics and Genomics group, Biogen Idec, Cambridge, MA, USA.
authors: Raghavendra
authors: R
authors: Hosur
authors: Suzanne
authors: S
authors: Szak
authors: Alice
authors: A
authors: Thai
authors: Norm
authors: N
authors: Allaire
authors: Jadwiga
authors: J
authors: Bienkowska
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020528:Multiple Sclerosis, Chronic Progressive / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D012984:Software