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  • 1
    Publication Date: 2015-05-01
    Description: 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.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 2
    Publication Date: 2015-05-09
    Description: Breast cancer (BC) is the second most common cancer among women. Research shows many women with BC experience anxiety, depression, and stress (ADS). Epigenetics has recently emerged as a potential mechanism for the development of depression.1 Although there are growing numbers of research studies indicating that epigenetic changes are associated with ADS, there is currently no evidence that this association is present in women with BC. The goal of this study was to identify high-throughput methylation sites (CpG sites) that are associated with three psychoneurological symptoms (ADS) in women with BC. Traditionally, univariate models have been used to examine the relationship between methylation sites and each psychoneurological symptom; nevertheless, ADS can be treated as a cluster of related symptoms and included together in a multivariate linear model. Hence, an overarching goal of this study is to compare and contrast univariate and multivariate models when identifying methylation sites associated with ADS in women with BC. When fitting separate linear regression models for each ADS scale, 3 among 285,173 CpG sites tested were significantly associated with depression. Two significant CpG sites are located on their respective genes FAM101A and FOXJ1, and the third site cannot be mapped to any known gene at this time. In contrast, the multivariate models identified 8,535 ADS-related CpG sites. In conclusion, when analyzing correlated psychoneurological symptom outcomes, multivariate models are more powerful and thus are recommended.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 3
    Publication Date: 2015-05-29
    Description: The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 4
    Publication Date: 2014-12-11
    Description: High-throughput genomic assays are performed using tissue samples with the goal of classifying the samples as normal 〈 pre-malignant 〈 malignant or by stage of cancer using a small set of molecular features. In such cases, molecular features monotonically associated with the ordinal response may be important to disease development; that is, an increase in the phenotypic level (stage of cancer) may be mechanistically linked through a monotonic association with gene expression or methylation levels. Though traditional ordinal response modeling methods exist, they assume independence among the predictor variables and require the number of samples (n) to exceed the number of covariates (P) included in the model. In this paper, we describe our ordinalgmifs R package, available from the Comprehensive R Archive Network, which can fit a variety of ordinal response models when the number of predictors (P) exceeds the sample size (n). R code illustrating usage is also provided.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 5
    Publication Date: 2015-09-02
    Description: No abstract supplied.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 6
    Publication Date: 2015-03-03
    Description: Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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