Other Computer Sciences Commons^™

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

LSU New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …

Towards Human Retinal Cones Spatial Distribution Modeling, Matteo Paolo Lanaro, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Alessandro Rizzi

MODVIS Workshop

No abstract provided.

A Virtual Reality System For Practicing Conversation Skills For Children With Autism, Natalia Stewart Rosenfield, Kathleen Lamkin, Jennifer Re, Kendra Day, Louanne E. Boyd, Erik J. Linstead

Engineering Faculty Articles and Research

We describe a virtual reality environment, Bob’s Fish Shop, which provides a system where users diagnosed with Autism Spectrum Disorder (ASD) can practice social interactions in a safe and controlled environment. A case study is presented which suggests such an environment can provide the opportunity for users to build the skills necessary to carry out a conversation without the fear of negative social consequences present in the physical world. Through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

The Ethics Of An Unlicensed Medical Practitioner, Charles C. Escott

Writing Across the Curriculum

For option A of this assignment, the prompt is that Harry, a manufacturer of medical equipment and an avid reader of medical textbooks, has developed a program that will allow its users to self-diagnose and self-treat their ailments, without a doctor’s help. Harry wants to sell his program to “ordinary folk” as a replacement for consulting licensed medical practitioners. An important point here is that Harry is not licensed to practice medicine and has only read books on the subject. The posed question is whether or not his program should be published (from an ethical standpoint—not necessarily a profit-driven one). …

Detecting And Mapping Real-Time Influenza-Like Illness Using Twitter Stream Data, Elisha D. Brunette

EWU Masters Thesis Collection

Influenza has been identified by the World Health Organization as a global issue that could be more effectively served through an accelerated and widely-accessible public health surveillance tracking process. The ability to map and predict influenza outbreaks in a real-time heat map would be invaluable to health care systems to prepare for influenza outbreaks. In this study, the Twitter stream data is filtered to identify potential influenza-like illness (ILI) cases. Then the tracking of real-time influenza cases is further explored and analyzed through various machine-learning models. Among seven learning models developed to identify ILI tweets, the ELMo deep neural network …

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Leveling The Playing Field: Supporting Neurodiversity Via Virtual Realities, Louanne E. Boyd, Kendra Day, Natalia Stewart, Kaitlyn Abdo, Kathleen Lamkin, Erik J. Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

Neurodiversity is a term that encapsulates the diverse expression of human neurology. By thinking in broad terms about neurological development, we can become focused on delivering a diverse set of design features to meet the needs of the human condition. In this work, we move toward developing virtual environments that support variations in sensory processing. If we understand that people have differences in sensory perception that result in their own unique sensory traits, many of which are clustered by diagnostic labels such as Autism Spectrum Disorder (ASD), Sensory Processing Disorder, Attention-Deficit/Hyperactivity Disorder, Rett syndrome, dyslexia, and so on, then we …

The Chapman Bone Algorithm: A Diagnostic Alternative For The Evaluation Of Osteoporosis, Elise Levesque, Anton Ketterer, Wajiha Memon, Cameron James, Noah Barrett, Cyril Rakovski, Frank Frisch

Mathematics, Physics, and Computer Science Faculty Articles and Research

Osteoporosis is the most common metabolic bone disease and goes largely undiagnosed throughout the world, due to the inaccessibility of DXA machines. Multivariate analyses of serum bone turnover markers were evaluated in 226 Orange County, California, residents with the intent to determine if serum osteocalcin and serum pyridinoline cross-links could be used to detect the onset of osteoporosis as effectively as a DXA scan. Descriptive analyses of the demographic and lab characteristics of the participants were performed through frequency, means and standard deviation estimations. We implemented logistic regression modeling to find the best classification algorithm for osteoporosis. All calculations and …

Expression Of The Microrna-143/145 Cluster Is Decreased In Hepatitis B Virus-Associated Hepatocellular Carcinoma And May Serve As A Biomarker For Tumorigenesis In Patients With Chronic Hepatitis B, Qi Zhao, Xiangfei Sun, Chao Liu, Tao Li, Juan Cui, Chengyong Qin

School of Computing: Faculty Publications

The aims of the present study were to identify the expression profile of microRNA (miR)‑143/145 in hepatitis B virus (HBV)‑associated hepatocellular carcinoma (HCC), explore its association with prognosis and investigate whether the serum miR‑143/145 expression levels may serve as a diagnostic indicator of HBV‑associated HCC. The microRNA (miRNA) chromatin immunoprecipitation dataset was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus databases, and analyzed using the Wilcoxon signed‑rank test. It was observed that the expression of miR‑143 and miR‑145 was decreased 1.5‑fold in HBV‑associated HCC samples compared with non‑tumor tissue in the TCGA and the GSE22058 datasets …

Atomistic Simulations And Network-Based Modeling Of The Hsp90-Cdc37 Chaperone Binding With Cdk4 Client Protein: A Mechanism Of Chaperoning Kinase Clients By Exploiting Weak Spots Of Intrinsically Dynamic Kinase Domains, John Czemeres, Kurt Buse, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

A fundamental role of the Hsp90 and Cdc37 chaperones in mediating conformational development and activation of diverse protein kinase clients is essential in signal transduction. There has been increasing evidence that the Hsp90-Cdc37 system executes its chaperoning duties by recognizing conformational instability of kinase clients and modulating their folding landscapes. The recent cryo-electron microscopy structure of the Hsp90-Cdc37- Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have combined atomistic simulations with protein stability and network-based rigidity decomposition analyses to characterize dynamic …

Virtual Reality As A Training Tool To Treat Physical Inactivity In Children, Adam W. Kiefer, David Pincus, Michael J. Richardson, Gregory D. Myer

Psychology Faculty Articles and Research

Lack of adequate physical activity in children is an epidemic that can result in obesity and other poor health outcomes across the lifespan. Physical activity interventions focused on motor skill competence continue to be developed, but some interventions, such as neuromuscular training (NMT), may be limited in how early they can be implemented due to dependence on the child’s level of cognitive and perceptual-motor development. Early implementation of motor-rich activities that support motor skill development in children is critical for the development of healthy levels of physical activity that carry through into adulthood. Virtual reality (VR) training may be beneficial …

A Cluster Analysis Of Challenging Behaviors In Autism Spectrum Disorder, Elizabeth Stevens, Abigail Atchison, Laura Stevens, Esther Hong, Doreen Granpeesheh, Dennis Dixon, Erik J. Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

We apply cluster analysis to a sample of 2,116 children with Autism Spectrum Disorder in order to identify patterns of challenging behaviors observed in home and centerbased clinical settings. The largest study of this type to date, and the first to employ machine learning, our results indicate that while the presence of multiple challenging behaviors is common, in most cases a dominant behavior emerges. Furthermore, the trend is also observed when we train our cluster models on the male and female samples separately. This work provides a basis for future studies to understand the relationship of challenging behavior profiles to …

An Evaluation Of The Effects Of Intensity And Duration On Outcomes Across Treatment Domains For Children With Autism Spectrum Disorder, Erik J. Linstead, D. R. Dixon, E. Hong, C. O. Burns, Ryan French, M. N. Novack, D. Granpeesheh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Applied behavior analysis (ABA) is considered an effective treatment for individuals with autism spectrum disorder (ASD), and many researchers have further investigated factors associated with treatment outcomes. However, few studies have focused on whether treatment intensity and duration have differential influences on separate skills. The aim of the current study was to investigate how treatment intensity and duration impact learning across different treatment domains, including academic, adaptive, cognitive, executive function, language, motor, play, and social. Separate multiple linear regression analyses were used to evaluate these relationships. Participants included 1468 children with ASD, ages 18 months to 12 years old, M= …

Computational Analysis Of Residue Interaction Networks And Coevolutionary Relationships In The Hsp70 Chaperones: A Community- Hopping Model Of Allosteric Regulation And Communication, Gabrielle Stetz, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks …

Near Real-Time Early Cancer Detection Using A Graphics Processing Unit, Jason Helms

EWU Masters Thesis Collection

"Automatically detecting early cancer using medical images is challenging, yet very crucial to help save millions of lives in the early stages of cancer. In this work, we improved a method that was originally developed by Yamaguchi et al. from the Saga University in Saga Japan. The original method would first decompose the endoscopic image into four color elements: red, green, blue and luminance (RGBL). Next each component is again decomposed to non-overlapping blocks of smaller images. Each smaller image undergoes two phases of DWT(s) and finally the Fractal Dimension (FD) is calculated per smaller image and abnormal regions are …

Electronic Medical Records (Emr): An Empirical Testing Of Factors Contributing To Healthcare Professionals’ Resistance To Use Emr Systems, Emmanuel Patrick Bazile

CCAC Theses and Dissertations

The benefits of using electronic medical records (EMRs) have been well documented; however, despite numerous financial benefits and cost reductions being offered by the federal government, some healthcare professionals have been reluctant to implement EMR systems. In fact, prior research provides evidence of failed EMR implementations due to resistance on the part of physicians, nurses, and clinical administrators. In 2010, only 25% of office-based physicians have basic EMR systems and only 10% have fully functional systems. One of the hindrances believed to be responsible for the slow implementation rates of EMR systems is resistance from healthcare professionals not truly convinced …

Do We Choose What We Desire? – Persuading Citizens To Make Consistent And Sustainable Mobility Decisions, Christopher Lisson, Margeret A. Hall

Interdisciplinary Informatics Faculty Proceedings & Presentations

A dilemma in urban mobility with tremendous effects on citizens’ wellbeing is the unconscious antipode between their short- and long-term goals. People do not anticipate all consequences of their modal choices and thus make decisions that might be incoherent with their desires, e.g. taking their own car due to convenience but causing a congested city. Omnipresent Information Systems on smartphones provide the necessary information and coordination capabilities to support people for sustainable and individually coherent mobility decisions on a mass scale. Building upon extant work in travel behavior and social psychology, a framework is proposed to coordinate research efforts in …

Umls::Association - Measuring The Association Between Biomedical Terms, Keith B. Herbert

UROP Posters

UMLS::Association - A Semantic Association Framework for Biomedical Texts

Keith Herbert

Natural Language Processing Lab, Department of Computer Science

Introduction

We present UMLS::Association, a software package to explore the semantic association of biomedical terms with applications for literature-based discovery. Literature-based discovery is an endeavour to ”connect the dots” for scientists between the topics of their research and those of unexpected relevance. However, many approaches rely on the exact wording for the ideas in the research papers being analyzed. The Unified Medical Language System (UMLS) provides a way to map natural language phrases in these papers to sequences of abstract yet …

Electronic Health Record Simulator, Samuel M. Brown

Williams Honors College, Honors Research Projects

The Electronic Health Record Simulator is exactly that; it is meant to simulate an EHR (like those found in hospitals and clinics) for use in an academic setting. Its purpose is to familiarize students with the operation of real-world EHRs in order to better prepare them for the workforce. It allows instructors to create fictional patients (with full lab values), and allows students to fill in information as in a real EHR.

Choice Of Human–Computer Interaction Mode In Stroke Rehabilitation, Hossein Mousavi Hondori, Maryam Khademi, Lucy Dodakian, Alison Mackenzie, Cristina V. Lopes, Steven C. Cramer

Physical Therapy Faculty Articles and Research

Background and Objective. Advances in technology are providing new forms of human–computer interaction. The current study examined one form of human–computer interaction, augmented reality (AR), whereby subjects train in the real-world workspace with virtual objects projected by the computer. Motor performances were compared with those obtained while subjects used a traditional human–computer interaction, that is, a personal computer (PC) with a mouse. Methods. Patients used goal-directed arm movements to play AR and PC versions of the Fruit Ninja video game. The 2 versions required the same arm movements to control the game but had different cognitive demands. With …

A Machine Learning Approach To Diagnosis Of Parkinson’S Disease, Sumaiya F. Hashmi

CMC Senior Theses

I will investigate applications of machine learning algorithms to medical data, adaptations of differences in data collection, and the use of ensemble techniques.

Focusing on the binary classification problem of Parkinson’s Disease (PD) diagnosis, I will apply machine learning algorithms to a primary dataset consisting of voice recordings from healthy and PD subjects. Specifically, I will use Artificial Neural Networks, Support Vector Machines, and an Ensemble Learning algorithm to reproduce results from [MS12] and [GM09].

Next, I will adapt a secondary regression dataset of PD recordings and combine it with the primary binary classification dataset, testing various techniques to consolidate …