ArticleCancer informatics2015
Generalized monotone incremental forward stagewise method for modeling count data: application predicting micronuclei frequency.
Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
- EMT activates exocytotic Rabs to coordinate invasion and immunosuppression in lung cancer.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Controlled variable selection in Weibull mixture cure models for high-dimensional data.Statistics in medicine · 2022Article
- DNA Damage/Repair Management in Cancers.Cancers · 2020Review
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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.
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