title: Predicting cancer type with dimensionality-reduced gene expression micro-array data.
abstract: Predictive modeling based on gene expression data is complicated by the high dimensionality (number of genes) of microarray data given the number of available samples. We investigate a method for reducing the dimensionality of the data using singular value decomposition.
authors.affiliation: Dept. of Computer Science, Tennessee Technological University, Cookeville, TN, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Santoro
authors.forename: Douglas A
authors.initials: DA
authors.lastname: Talbert
mesh_terms: D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D012680:Sensitivity and Specificity