title: pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery.
abstract: Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data.
  
  
  BACKGROUND
  pathClass is open source and freely available as an R-Package on the CRAN repository at http://cran.r-project.org.
authors: German Cancer Research Center, Cancer Genome Research, Heidelberg, Germany. m.johannes@dkfz-heidelberg.de
authors: Marc
authors: M
authors: Johannes
authors: Holger
authors: H
authors: Fröhlich
authors: Holger
authors: H
authors: Sültmann
authors: Tim
authors: T
authors: Beissbarth
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis* / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000503:physiopathology; D011379:Prognosis
chemical_list: D015415:Biomarkers