title: Generalized monotone incremental forward stagewise method for modeling count data: application predicting micronuclei frequency.
abstract: The cytokinesis-block micronucleus (CBMN) assay can be used to quantify micronucleus (MN) formation, the outcome measured being MN frequency. MN frequency has been shown to be both an accurate measure of chromosomal instability/DNA damage and a risk factor for cancer. Similarly, the Agilent 4×44k human oligonucleotide microarray can be used to quantify gene expression changes. Despite the existence of accepted methodologies to quantify both MN frequency and gene expression, very little is known about the association between the two. In modeling our count outcome (MN frequency) using gene expression levels from the high-throughput assay as our predictor variables, there are many more variables than observations. Hence, we extended the generalized monotone incremental forward stagewise method for predicting a count outcome for high-dimensional feature settings.
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Mateusz
authors.initials: M
authors.lastname: Makowski
authors.affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: Kellie J
authors.initials: KJ
authors.lastname: Archer
keywords: Poisson regression; gene expression; high-throughput; micronuclei; penalized model