title: Module-based prediction approach for robust inter-study predictions in microarray data.
abstract: BACKGROUND
  Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering.
  
  
  RESULTS
  K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP.
  
  
  BACKGROUND
  http://www.biostat.pitt.edu/bioinfo/
  
  
  BACKGROUND
  ctseng@pitt.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Cooperative Studies Program, VA Maryland Health Care System, Perry Point, MD 21902, USA.
authors: Zhibao
authors: Z
authors: Mi
authors: Kui
authors: K
authors: Shen
authors: Nan
authors: N
authors: Song
authors: Chunrong
authors: C
authors: Cheng
authors: Chi
authors: C
authors: Song
authors: Naftali
authors: N
authors: Kaminski
authors: George C
authors: GC
authors: Tseng
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D020869:Gene Expression Profiling / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*