title: Building gene expression profile classifiers with a simple and efficient rejection option in R.
abstract: BACKGROUND
  The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers.
  
  
  RESULTS
  This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention.
  
  
  CONCLUSIONS
  This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be available.
authors.affiliation: Control and Computer Engineering Department, Politecnico di Torino, Corso Duca degli Abruzzi 24,10129, Torino, Italy.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Benso
authors.forename: Stefano
authors.initials: S
authors.lastname: Di Carlo
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Politano
authors.forename: Alessandro
authors.initials: A
authors.lastname: Savino
authors.forename: Hafeez
authors.initials: H
authors.lastname: Hafeezurrehman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling; D006801:Humans; D008223:Lymphoma / Q000145:classification / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results