title: Predicting breast cancer using an expression values weighted clinical classifier.
abstract: BACKGROUND
  Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.
  
  
  RESULTS
  We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.
  
  
  CONCLUSIONS
  Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.
authors.affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. minta.thomas@esat.kuleuven.be.
authors.forename: Minta
authors.initials: M
authors.lastname: Thomas
authors.affiliation: Department of Statistics & Computer Science, Iowa State University, Ames, IA, USA. kris.debrabanter@esat.kuleuven.be.
authors.forename: Kris
authors.initials: K
authors.lastname: De Brabanter
authors.affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. Johan.Suykens@esat.kuleuven.be.
authors.forename: Johan A K
authors.initials: JA
authors.lastname: Suykens
authors.affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. bart.demoor@esat.kuleuven.be.
authors.forename: Bart
authors.initials: B
authors.lastname: De Moor
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics*; D030541:Databases, Genetic; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012984:Software; D060388:Support Vector Machine