Other Statistics and Probability Commons^™

Existing And Potential Statistical And Computational Approaches For The Analysis Of 3d Ct Images Of Plant Roots, Zheng Xu, Camilo Valdes, Jennifer Clarke

Department of Statistics: Faculty Publications

Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been …

Characterization Of Soybean Protein Adhesives Modified By Xanthan Gum, Chen Feng, Fang Wang, Zheng Xu, Huilin Sui, Yong Fang, Xiaozhi Tang, Xinchun Shen

Department of Statistics: Faculty Publications

The aim of this study was to provide a basis for the preparation of medical adhesives from soybean protein sources. Soybean protein (SP) adhesives mixed with different concentrations of xanthan gum (XG) were prepared. Their adhesive features were evaluated by physicochemical parameters and an in vitro bone adhesion assay. The results showed that the maximal adhesion strength was achieved in 5% SP adhesive with 0.5% XG addition, which was 2.6-fold higher than the SP alone. The addition of XG significantly increased the hydrogen bond and viscosity, as well as increased the β-sheet content but decreased the α-helix content in the …

Development Of 11-Plex Mol-Pcr Assay For The Rapid Screening Of Samples For Shiga Toxin-Producing Escherichia Coli, Travis A. Woods, Heather M. Mendez, Sandy Ortega, Xiaorong Shi, David Marx, Jianfa Bai, Rodney A. Moxley, T. G. Nagaraja, Steven W. Graves, Alina Deshpande

Department of Statistics: Faculty Publications

Strains of Shiga toxin-producing Escherichia coli (STEC) are a serious threat to the health, with approximately half of the STEC related food-borne illnesses attributable to contaminated beef. We developed an assay that was able to screen samples for several important STEC associated serogroups (O26, O45, O103, O104, O111, O121, O145, O157) and three major virulence factors (eae, stx1, stx2) in a rapid and multiplexed format using the Multiplex oligonucleotide ligation-PCR (MOL-PCR) assay chemistry. This assay detected unique STEC DNA signatures and is meant to be used on samples from various sources related to beef production, providing a multiplex and high-throughput …

Application Of Transfer Learning For Cancer Drug Sensitivity Prediction, Saugato Rahman Dhruba, Raziur Rahman, Kevin Matlock, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.

Results: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first …

Investigation Of Model Stacking For Drug Sensitivity Prediction, Kevin Matlock, Carlos De Niz, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.

Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing …

Sequential Probing With A Random Start, Joshua Miller

HMC Senior Theses

Processing user requests quickly requires not only fast servers, but also demands methods to quickly locate idle servers to process those requests. Methods of finding idle servers are analogous to open addressing in hash tables, but with the key difference that servers may return to an idle state after having been busy rather than staying busy. Probing sequences for open addressing are well-studied, but algorithms for locating idle servers are less understood. We investigate sequential probing with a random start as a method for finding idle servers, especially in cases of heavy traffic. We present a procedure for finding the …

Some New And Generalized Distributions Via Exponentiation, Gamma And Marshall-Olkin Generators With Applications, Hameed Abiodun Jimoh

College of Graduate Studies: Theses & Dissertations

Three new generalized distributions developed via completing risk, gamma generator, Marshall-Olkin generator and exponentiation techniques are proposed and studied. Structural properties including quantile functions, hazard rate functions, moment, conditional moments, mean deviations, R\'enyi entropy, distribution of order statistics and maximum likelihood estimates are presented. Monte Carlo simulation is employed to examine the performance of the proposed distributions. Applications of the generalized distributions to real lifetime data are presented to illustrate the usefulness of the models.

Effect Of Neuromodulation Of Short-Term Plasticity On Information Processing In Hippocampal Interneuron Synapses, Elham Bayat Mokhtari

Graduate Student Theses, Dissertations, & Professional Papers

Neurons convey information about the complex dynamic environment in the form of signals. Computational neuroscience provides a theoretical foundation toward enhancing our understanding of nervous system. The aim of this dissertation is to present techniques to study the brain and how it processes information in particular neurons in hippocampus.

We begin with a brief review of the history of neuroscience and biological background of basic neurons. To appreciate the importance of information theory, familiarity with the information theoretic basics is required, these basics are presented in Chapter 2. In Chapter 3, we use information theory to estimate the amount of …

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

College of Graduate Studies: Theses & Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

College of Graduate Studies: Theses & Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Making Models With Bayes, Pilar Olid

Electronic Theses, Projects, and Dissertations

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects …

Open Source Artificial Intelligence In A Biological/Ecological Context, Trevor Grant

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Discrete Stochastic Modeling For First-Year Biology Students, Dmitry Kondrashov

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Investigating The Student Enrollment Decision At Wku, Alec Brown

Mahurin Honors College Capstone Experience/Thesis Projects

The purpose of this research is to investigate the relationships between the enrollment decision of first-time, first-year students admitted to Western Kentucky University and the amount of financial aid awarded, as well as demographic information. The Division of Enrollment Management provided a SAS dataset containing various information about all WKU students admitted in 2013, 2014, and 2015. Additionally, information about the 2016 class of admitted students was provided. The data has been analyzed in SAS Enterprise Miner. We performed analysis using decision tree modeling and logistic regression modeling. Results of these two procedures indicated the importance of credit hours earned …

Heterogeneity Aware Random Forest For Drug Sensitivity Prediction, Raziur Rahman, Kevin Matlock, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea …

Imputation For Random Forests, Joshua Young

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

This project introduces two new methods for imputation of missing data in random forests. The new methods are compared against other frequently used imputation methods, including those used in the randomForest package in R. To test the effectiveness of these methods, missing data are imputed into datasets that contain two missing data mechanisms including missing at random and missing completely at random. After imputation, random forests are run on the data and accuracies for the predictions are obtained. Speed is an important aspect in computing; the speeds for all the tested methods are also compared.

One of the new methods …

On The Analysis Of The Sir Epidemic Model For Small Networks: An Application In Hospital Settings, Martin Lopez-Garcia

Biology and Medicine Through Mathematics Conference

No abstract provided.

Can Cone Signals In The Wild Be Predicted From The Past?, David H. Foster, Iván Marín-Franch

MODVIS Workshop

In the natural world, the past is usually a good guide to the future. If light from the sun and sky is blue earlier in the day and yellow now, then it is likely to be more yellow later, as the sun's elevation decreases. But is the light reflected from a scene into the eye as predictable as the light incident upon the scene, especially when lighting changes are not just spectral but include changes in local shadows and mutual reflections? The aim of this work was to test the predictability of cone photoreceptor signals in the wild over the …

Gilmore Girls And Instagram: A Statistical Look At The Popularity Of The Television Show Through The Lens Of An Instagram Page, Brittany Simmons

Student Scholar Symposium Abstracts and Posters

After going on the Warner Brothers Tour in December of 2015, I created a Gilmore Girls Instagram account. This account, which started off as a way for me to create edits of the show and post my photos from the tour turned into something bigger than I ever could have imagined. In just over a year I have over 55,000 followers. I post content including revival news, merchandise, and edits of the show that have been featured in Entertainment Weekly, Bustle, E! News, People Magazine, Yahoo News, & GilmoreNews.

I created a dataset of qualitative and quantitative outcomes from my …

The Value Of A Win: Analysis Of Playoff Structures, Matthew Orsi

Honors Projects in Mathematics

The purpose of this Senior Capstone project is to analyze the distinctions between existing playoff systems. In particular, we are looking to analyze the differences between the standard single-elimination tournament (which the NCAA has used since the inception of the tournament) and other potential options: double-elimination and multiple game series. Popular sports such as Major League Baseball and the National Basketball Association all use multiple game series for their playoffs. This project will use probability theory and simulation to determine the likelihood of different seeds winning a championship as well as the expected number of victories by seed in each …