Classification with high-dimensional genetic data: assigning patients and genetic features to known classes.
A major task in the statistical analysis of genetic data such as gene expressions and single nucleotide polymorphisms (SNPs) is to predict whether a patient has a certain disease, or from which of several known subtypes of a disease a patient suffers. A large number of discrimination methods have been proposed in the literature and have been applied to genetic data to tackle this task. In this paper, we give an overview on the most popular of these procedures in the analysis of genetic data. Moreover, we describe how these methods for supervised classification can be combined with variable selection approaches to reduce the number of genetic features from several thousands to as few as possible to form a concise classification rule. Finally, we show how the resulting statistical models can be validated.
Schwender
Holger
H
Technische Universität Dortmund, Fakultät Statistik, Vogelpothsweg 87, Dortmund, Germany.
Ickstadt
Katja
K
Rahnenführer
Jörg
J
D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics; D003627:Data Interpretation, Statistical; D004194:Disease / Q000235:genetics*; D020869:Gene Expression Profiling; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020641:Polymorphism, Single Nucleotide
D004247:DNA