title: Classification of cancer patients using pathway analysis and network clustering.
abstract: Molecular expression patterns have often been used for patient classification in oncology in an effort to improve prognostic prediction and treatment compatibility. This effort is, however, hampered by the highly heterogeneous data often seen in the molecular analysis of cancer. The lack of overall similarity between expression profiles makes it difficult to partition data using conventional data mining tools. In this chapter, the authors introduce a bioinformatics protocol that uses REACTOME pathways and patient-protein network structure (also called topology) as the basis for patient classification.
authors.affiliation: School of Biotechnology and Biomolecular Sciences, New South Wales Systems Biology Initiative, The University of New South Wales, Sydney, NSW, Australia.
authors.forename: David C Y
authors.initials: DC
authors.lastname: Fung
authors.forename: Amy
authors.initials: A
authors.lastname: Lo
authors.forename: Lucy
authors.initials: L
authors.lastname: Jankova
authors.forename: Stephan J
authors.initials: SJ
authors.lastname: Clarke
authors.forename: Mark
authors.initials: M
authors.lastname: Molloy
authors.forename: Graham R
authors.initials: GR
authors.lastname: Robertson
authors.forename: Marc R
authors.initials: MR
authors.lastname: Wilkins
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism* / Q000628:therapy; D015513:Oncogene Proteins / Q000235:genetics / Q000378:metabolism; D012984:Software; D053719:Tandem Mass Spectrometry
chemical_list: D014408:Biomarkers, Tumor; D015513:Oncogene Proteins