title: Classification and Clustering on Microarray Data for Gene Functional Prediction Using R.
abstract: Gene expression data (microarrays and RNA-sequencing data) as well as other kinds of genomic data can be extracted from publicly available genomic data. Here, we explain how to apply multivariate cluster and classification methods on gene expression data. These methods have become very popular and are implemented in freely available software in order to predict the participation of gene products in a specific functional category of interest. Taking into account the availability of data and of these methods, every biological study should apply them in order to obtain knowledge on the organism studied and functional category of interest. A special emphasis is made on the nonlinear kernel classification methods.
authors.forename: Liliana
authors.initials: L
authors.lastname: López-Kleine
authors.affiliation: Departamento de Estadística, Universidad Nacional de Colombia, Edificio 404, Oficina 342, Carreara 45 No 26-28, Bogotá, DC, Colombia. llopezk@unal.edu.co.
authors.forename: Liliana López
authors.initials: LL
authors.lastname: Kleine
authors.affiliation: Departamento de Matemática y Ciencia de la Computación, Universidad de Santiago de Chile, Santiago, Chile.
authors.forename: Rosa
authors.initials: R
authors.lastname: Montaño
authors.affiliation: Departamento de Matemática y Ciencia de la Computación, Universidad de Santiago de Chile, Santiago, Chile.
authors.forename: Francisco
authors.initials: F
authors.lastname: Torres-Avilés
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D060388:Support Vector Machine
keywords: Classification; Clustering; Functional prediction; Microarrays; Multivariate data analysis