title: Gene network modular-based classification of microarray samples.
abstract: BACKGROUND
  Molecular predictor is a new tool for disease diagnosis, which uses gene expression to classify diagnostic category of a patient. The statistical challenge for constructing such a predictor is that there are thousands of genes to predict for the disease categories, but only a small number of samples are available.
  
  
  RESULTS
  We proposed a gene network modular-based linear discriminant analysis approach by integrating 'essential' correlation structure among genes into the predictor in order that the modules or cluster structures of genes, which are related to the diagnostic classes we look for, can have potential biological interpretation. We evaluated performance of the new method with other established classification methods using three real data sets.
  
  
  CONCLUSIONS
  Our results show that the new approach has the advantage of computational simplicity and efficiency with relatively lower classification error rates than the compared methods in many cases. The modular-based linear discriminant analysis approach induced in the study has the potential to increase the power of discriminant analysis for which sample sizes are small and there are large number of genes in the microarray studies.
authors: Department of Computer Science and Engineering, York University, Toronto, M3J 1P3, Canada. phu@cse.yorku.ca
authors: Pingzhao
authors: P
authors: Hu
authors: Shelley B
authors: SB
authors: Bull
authors: Hui
authors: H
authors: Jiang
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification* / Q000379:methods