Data Science Commons^™

Exploration Of Preterm Birth Rates Using The Public Health Exposome Database And Computational Analysis Methods, Anne D. Kershenbaum, Michael A. Langston, Robert S. Levine, Arnold M. Saxton, Tonny J. Oyana, Barbara J. Kilbourne, Gary L. Rogers, Lisaann S. Gittner, Suzanne H. Baktash, Patricia Matthews-Juarez, Paul D. Juarez

Sociology Faculty Research

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used …

Scalable Combinatorial Tools For Health Disparities Research, Michael A. Langston, Robert S. Levine, Barbara J. Kilbourne, Gary L. Rogers Jr., Anne D. Kershenbaum, Suzanne H. Baktash, Steven S. Coughlin, Arnold M. Saxton, Vincent K. Agboto, Darryl B. Hood, Maureen Y. Litchveld, Tonny J. Oyana, Patricia Matthews-Juarez, Paul D. Juarez

Sociology Faculty Research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size …

Data Completion Methods For Improved Developmental Stage Annotation Of Drosophila Embryos In Images, Chitsanu Janyalikit

Electrical & Computer Engineering Theses & Dissertations

Drosophila melanogaster is a dominant model organism for studying the function of animal genes in initial stages of embryogenesis. Usually, images containing Drosophila gene expression patterns are captured at different developmental stages to study the interconnection of animal genes. To achieve most biologically meaningful results, gene expression images from a similar stage should be compared. Currently, biologists manually classify embryos in images into different stages, which is time intensive and infeasible for current massively produced gene expression images. Therefore, there is a need to develop an automatic system for the annotation.

Gene expression information in embryo images usually appears as …

Using Weka To Mine Temporal Work Patterns Of Programming Students, Dale E. Parson

Computer Science and Information Technology Faculty

Using Weka to Mine Temporal Work Patterns of Programming Students consists of notes on analyzing datasets using the Weka tool presented at the July 2014 FECS'14 Conference in Las Vegas.

Assessing Organizational Effectiveness Through The Competing Values Framework A Data Envelopment Approach, Raghavender Macherla

Engineering Management & Systems Engineering Theses & Dissertations

This study proposes a model to diagnose organizations using the mathematical principles of data envelopment analysis (DEA) to the variables generated using competing values framework (CVF) in order to evaluate overall organizational effectiveness. The notion of organizational effectiveness is abstract and difficult to measure due to its complexity and multi-functional nature. Over the years, measurement of organizational effectiveness has remained a challenge due to the lack of agreement on the factors that should be assessed to determine effectiveness. This research is aimed at shedding some light into this topic by using data envelopment analysis as a tool to measure relative …

Visualization Of Multidimensional Data With Collocated Paired Coordinates And General Line Coordinates, Boris Kovalerchuk

All Faculty Scholarship for the College of the Sciences

Often multidimensional data are visualized by splitting n-D data to a set of low dimensional data. While it is useful it destroys integrity of n-D data, and leads to a shallow understanding complex n-D data. To mitigate this challenge a difficult perceptual task of assembling low-dimensional visualized pieces to the whole n-D vectors must be solved. Another way is a lossy dimension reduction by mapping n-D vectors to 2-D vectors (e.g., Principal Component Analysis). Such 2-D vectors carry only a part of information from n-D vectors, without a way to restore n-D vectors exactly from it. An alternative way for …

Integrating Societal Perspectives And Values For Improved Stewardship Of A Coastal Ecosystem Engineer, Steven B. Scyphers, J. Steven Picou, Robert D. Brumbaugh, Sean P. Powers

University Faculty and Staff Publications

Oyster reefs provide coastal societies with a vast array of ecosystem services, but are also destructively harvested as an economically and culturally important fishery resource, exemplifying a complex social-ecological system (SES). Historically, societal demand for oysters has led to destructive and unsustainable levels of harvest, which coupled with multiple other stressors has placed oyster reefs among the most globally imperiled coastal habitats. However, more recent studies have demonstrated that large-scale restoration is possible and that healthy oyster populations can be sustained with effective governance and stewardship. However, both of these require significant societal support or financial investment. In our study, …

Semantics In Support Of Biodiversity Knowledge Discovery: An Introduction To The Biological Collections Ontology And Related Ontologies, Ramona L. Walls, John Deck, Robert Guralnick, Steve Baskauf, Reed Beaman, Stanley Blum, Shaun Bowers

Computer Science Faculty Scholarship

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to …

Evaluating The Impact Of Genotype Errors On Rare Variant Tests Of Association, Kaitlyn Cook, Alejandra Benitez, Casey Fu, Nathan Tintle

Statistical and Data Sciences: Faculty Publications

The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an …

Information In Biological Systems And The Fluctuation Theorem, Yaşar Demirel

Department of Chemical and Biomolecular Engineering: Faculty Publications

Some critical trends in information theory, its role in living systems and utilization in fluctuation theory are discussed. The mutual information of thermodynamic coupling is incorporated into the generalized fluctuation theorem by using information theory and nonequilibrium thermodynamics. Thermodynamically coupled dissipative structures in living systems are capable of degrading more energy, and processing complex information through developmental and environmental constraints. The generalized fluctuation theorem can quantify the hysteresis observed in the amount of the irreversible work in nonequilibrium regimes in the presence of information and thermodynamic coupling.

The Bivariate Erlang And Its Application In Modeling Recurrence Times Of Kidney Dialysis Data, Norou Diawara, S.H. Sathish Indika, Melva Grant, Edgard M. Maboudou-Tchao

Mathematics & Statistics Faculty Publications

Recent advances in computer modeling allows us to find closer fits to data. Our emphasis is on the interdependence between occurrence at kidney dialysis. The interdependence between kidney dialysis occurrences is modelled by a bivariate exponential that we propose in this article. The application is shown on the McGilchrist and Aisbett kidney data set with the use of the exponential distribution. The proposed bivariate exponential model has exponential marginal densities, correlated via a latent random variables and with finite probability of simultaneous occurrence. Extension of the model to a bivariate Erlang type distribution with same shape parameter is presented.

Classification With Hidden Markov Model, Badreddine Benyacoub, Souad Elbernoussi, Abdelhak Zoglat, Ismail El Moudden

Research and Infrastructure Service Enterprise (RISE) Faculty Publications

Classification and statistical learning by hidden markov model has achieved remarkable progress in the past decade. They have been applied in many areas like speech recognition and handwriting recognition. However, learning by Hidden Markov Model (HMM) is still restricted to supervised problems. In this paper, we propose a new learning method based on HMM techniques estimations, to built a model for classification. The approach consists of evaluation of the probability to belonging in one group, given the observations by a linear classifier. Our developed algorithm is based on discrete states and discrete observations cases of HMM. Experimental results show that …