title: RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes.
abstract: BACKGROUND
  Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.
  
  
  METHODS
  We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER- breast cancer samples.
  
  
  RESULTS
  The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.
authors.affiliation: School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia.
authors.forename: Ashish
authors.identifier: 0000-0002-3723-0809
authors.initials: A
authors.lastname: Saini
authors.affiliation: School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia.
authors.forename: Jingyu
authors.initials: J
authors.lastname: Hou
authors.affiliation: School of Information Technology, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia.
authors.forename: Wanlei
authors.initials: W
authors.lastname: Zhou
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics / Q000378:metabolism; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011960:Receptors, Estrogen / Q000096:biosynthesis / Q000235:genetics
chemical_list: D011960:Receptors, Estrogen