title: Developing classifiers for the detection of cancer using multi-analytes.
abstract: The development of a successful classifier from multiple predictors (analytes) is a multistage process complicated typically by the paucity of the data samples when compared to the number of available predictors. Choosing an adequate validation strategy is key for drawing sound conclusions about the usefulness of the classifier. Other important decisions have to be made regarding the type of prediction model to be used and training algorithm, as well as the way in which the markers are selected. This chapter describes the principles of the classifier development and underlines the most common pitfalls. A simulated dataset is used to illustrate the main concepts involved in supervised classification.
authors.affiliation: Department of Computer Science, Wayne State University, Detroit, MI, USA.
authors.forename: Adi Laurentiu
authors.initials: AL
authors.lastname: Tarca
authors.forename: Sorin
authors.initials: S
authors.lastname: Draghici
authors.forename: Roberto
authors.initials: R
authors.lastname: Romero
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D008967:Molecular Biology / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor