title: Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer.
abstract: BACKGROUND
  Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed.
  
  
  RESULTS
  We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others.
  
  
  BACKGROUND
  the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm
authors.affiliation: Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR. ifkorf@ucdavis.edu
authors.forename: D
authors.initials: D
authors.lastname: Huang
authors.forename: Tommy W S
authors.initials: TW
authors.lastname: Chow
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics*