Statistical Models Commons^™

Modeling Biphasic, Non-Sigmoidal Dose-Response Relationships: Comparison Of Brain- Cousens And Cedergreen Models For A Biochemical Dataset, Venkat D. Abbaraju, Tamaraty L. Robinson, Brian P. Weiser

Rowan-Virtua School of Osteopathic Medicine Faculty Scholarship

Biphasic, non-sigmoidal dose-response relationships are frequently observed in biochemistry and pharmacology, but they are not always analyzed with appropriate statistical methods. Here, we examine curve fitting methods for “hormetic” dose-response relationships where low and high doses of an effector produce opposite responses. We provide the full dataset used for modeling, and we provide the code for analyzing the dataset in SAS using two established mathematical models of hormesis, the Brain-Cousens model and the Cedergreen model. We show how to obtain and interpret curve parameters such as the ED50 that arise from modeling, and we discuss how curve parameters might change …

Determining Malignancy: Can Mammogram Results Help Predict The Diagnosis Of Breast Tumors?, Taylor Behrens

Symposium of Student Scholars

Even with advancements in treatment and preventative care, breast cancer remains an epidemic claiming more than 40,000 American male and female lives each year. The mammogram dataset that I am analyzing was initially complied in the early 1990s by a team from the University of Wisconsin - Madison. Past research diagnoses breast cancer from fine-needle aspirates. My research focuses on predicting whether we can determine breast cancer diagnoses without the use of invasive procedures and, in particular, whether we can predict breast cancer based on mammogram data. Do measures of gray-scale texture, radius, concavity, perimeter, compactness, area, and smoothness of …

Genomic Neighborhoods For Arabidopsisretrotransposons: A Role For Targeted Integration In The Distribution Of The Metaviridae, Brooke D. Peterson-Burch, Dan Nettleton, Daniel F. Voytas

Dan Nettleton

Background: Retrotransposons are an abundant component of eukaryotic genomes. The high quality of the Arabidopsis thaliana genome sequence makes it possible to comprehensively characterize retroelement populations and explore factors that contribute to their genomic distribution.

Results: We identified the full complement of A. thaliana long terminal repeat (LTR) retroelements using RetroMap, a software tool that iteratively searches genome sequences for reverse transcriptases and then defines retroelement insertions. Relative ages of full-length elements were estimated by assessing sequence divergence between LTRs: the Pseudoviridae were significantly younger than the Metaviridae. All retroelement insertions were mapped onto the genome sequence and their distribution …

Non-Syntenic Genes Drive Rtcs-Dependent Regulation Of The Embryo Transcriptome During Formation Of Seminal Root Primordia In Maize (Zea Mays L.), Huanhuan Tai, Nina Opitz, Andrew Lithio, Xin Lu, Dan Nettleton, Frank Hochholdinger

Dan Nettleton

Seminal roots of maize are pivotal for early seedling establishment. The maize mutant rootless concerning crown and seminal roots (rtcs) is defective in seminal root initiation during embryogenesis. In this study, the transcriptomes of wild-type and rtcs embryos were analyzed by RNA-Seq based on histological results at three stages of seminal root primordia formation. Hierarchical clustering highlighted that samples of each genotype grouped together along development. Determination of their gene activity status revealed hundreds of genes specifically transcribed in wild-type or rtcs embryos, while K-mean clustering revealed changes in gene expression dynamics between wild-type and rtcs during embryo …

Unified Methods For Feature Selection In Large-Scale Genomic Studies With Censored Survival Outcomes, Lauren Spirko-Burns, Karthik Devarajan

COBRA Preprint Series

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease's process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous data sets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …

Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato

Dissertations & Theses (Open Access)

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. …