title: Discovery of highly differentiative gene groups from microarray gene expression data using the gene club approach.
abstract: BACKGROUND
  It is commonly believed that suitable analysis of microarray gene expression profile data can lead to better understanding of diseases, and better ways to diagnose and treat diseases. To achieve those goals, it is of interest to discover the gene interaction networks, and perhaps even pathways, underlying given diseases from such data. In this paper, we consider methods for efficiently discovering highly differentiative gene groups (HDGG), which may provide insights on gene interaction networks. HDGGs are groups of genes which completely or nearly completely characterize the diseased or normal tissues. Discovering HDGGs is challenging, due to the high dimensionality of the data.
  
  
  RESULTS
  Our methods are based on the novel concept of gene clubs. A gene club consists of a set of genes having high potential to be interactive with each other. The methods can (i) efficiently discover signature HDGGs which completely characterize the diseased and the normal tissues respectively, (ii) find strongest or near strongest HDGGs containing any given gene, and (iii) find much stronger HDGGs than previous methods. As part of the experimental evaluation, the methods are applied to colon, prostate, ovarian, and breast cancer, and leukemia and so on. Some of the genes in the extracted signature HDGGs have known biological functions, and some have attracted little attention in biology and medicine. We hope that appropriate study on them can lead to medical breakthroughs. Some HDGGs for colon and prostate cancers are listed here. The website listed below contains HDGGs for the other cancers.
  
  
  BACKGROUND
  HDGG is implemented in C++ and runs on Unix or Windows platform. The code is available at: http://www.cs.wright.edu/~gdong/hdgg/.
authors.affiliation: Department of Computer Science and Engineering, Wright State University, USA. smao@cs.wright.edu
authors.forename: Shihong
authors.initials: S
authors.lastname: Mao
authors.forename: Guozhu
authors.initials: G
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
chemical_list: D009363:Neoplasm Proteins