Translational Medical Research Commons^™

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab

Electronic Theses and Dissertations

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations & Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Interpreting Health Events In Big Data Using Qualitative Traditions, Roschelle L. Fritz, Gordana Dermody

Research outputs 2014 to 2021

© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. …

Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …

The Pharmacogene Variation (Pharmvar) Consortium: Incorporation Of The Human Cytochrome P450 (Cyp) Allele Nomenclature Database, Andrea Gaedigk, Magnus Ingelman-Sundberg, Neil A. Miller, J Steven Leeder, Michelle Whirl-Carrillo, Teri E. Klein

Manuscripts, Articles, Book Chapters and Other Papers

The Human Cytochrome P450 (CYP) Allele Nomenclature Database, a critical resource to the pharmacogenetics and genomics communities, will be transitioning to the Pharmacogene Variation (PharmVar) Consortium. In this report we provide a summary of the current database, provide an overview of the PharmVar consortium and highlight the PharmVar database which will serve as the new home for pharmacogene nomenclature.

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Network Inference Driven Drug Discovery, Gergely Zahoránszky-Kőhalmi, Tudor I. Oprea Md, Phd, Cristian G. Bologa Phd, Subramani Mani Md, Phd, Oleg Ursu Phd

Biomedical Sciences ETDs

The application of rational drug design principles in the era of network-pharmacology requires the investigation of drug-target and target-target interactions in order to design new drugs. The presented research was aimed at developing novel computational methods that enable the efficient analysis of complex biomedical data and to promote the hypothesis generation in the context of translational research. The three chapters of the Dissertation relate to various segments of drug discovery and development process.

The first chapter introduces the integrated predictive drug discovery platform „SmartGraph”. The novel collaborative-filtering based algorithm „Target Based Recommender (TBR)” was developed in the framework of this …

Providing Hands-On Training With Bioinformatics Databases: A Collaboration Between Vcu Libraries & Wright Center For Clinical And Translational Research, Karen H. Gau, Julie A. Arendt, Amy Olex, Aaron R. Wolen

VCU Libraries Faculty and Staff Presentations

Background
With the goal of increasing specialized services for researchers, Virginia Commonwealth University (VCU) Libraries sent its basic science librarians to an intensive training on bioinformatics databases, “A Librarian’s Guide to NCBI.” VCU’s Wright Center for Clinical and Translational Research (Wright CCTR) was expanding the educational component of its bioinformatics support around the same time. This year, the librarians partnered with the Wright CCTR to offer an introductory bioinformatics database workshop introducing researchers to genetic/genomic databases.

Methods
For one week in June, sessions were conducted introducing up to 30 faculty and staff to The Cancer Genome Atlas and NCBI’s Gene, …

Characterizing The Performance And Behaviors Of Runners Using Twitter, Qian He, Emmanuel Agu, Diane Strong, Bengisu Tulu, Peder Pedersen

Emmanuel O. Agu

Running is a popular physical activity that improves physical and mental wellbeing. Unfortunately, up-to- date information about runners’ performance and psychological wellbeing is limited. Many questions remain unanswered, such as how far and how fast runners typically run, their preferred running times and frequencies, how long new runners persist before dropping out, and what factors cause runners to quit. Without hard data, establishing patterns of runner behavior and mitigating the challenges they face are difficult. Collecting data manually from large numbers of runners for research studies is costly and time consuming. Emerging Social Networking Services (SNS) and fitness tracking devices …

Detection Of Diabetic Foot Ulcers Using Svm Based Classification, Lei Wang, Peder Pedersen, Diane Strong, Bengisu Tulu, Emmanuel Agu, Qian He, Ronald Ignotz, Raymond Dunn, David Harlan, Sherry Pagoto

Emmanuel O. Agu

Diabetic foot ulcers represent a significant health issue, for both patients’ quality of life and healthcare system costs. Currently, wound care is mainly based on visual assessment of wound size, which suffers from lack of accuracy and consistency. Hence, a more quantitative and computer-based method is needed. Supervised machine learning based object recognition is an attractive option, using training sample images with boundaries labeled by experienced clinicians. We use forty sample images collected from the UMASS Wound Clinic by tracking 8 subjects over 6 months with a smartphone camera. To maintain a consistent imaging environment and facilitate the capture process …

A Context-Aware Activity Recommendation Smartphone Application To Mitigate Sedentary Lifestyles, Qian He, Emmanuel Agu

Emmanuel O. Agu

A sedentary lifestyle involves irregular or no physical activity. In this kind of lifestyle, people’s activities do not increase their energy expenditure substantially above resting levels. Long periods of sitting, lying, watching television, playing video games, and using the computer are typical examples. Energy expenditures at 1.0-1.5 Metabolic Equivalent Units (METs) are considered sedentary behaviors. A recent study of sedentary lifestyles found that the length of sedentary times is associated with an increased risk of diabetes, cardiovascular disease, and cardiovascular and all-cause mortality. In this study, we developed a smartphone application called “On11”, which continuously tracks and informs the user …

Prediction Of Laser Ablation In Brain: Sensitivity, Calibration, And Validation, Samuel J. Fahrenholtz

Dissertations & Theses (Open Access)

The surgical planning of MR-guided laser induced thermal therapy (MRgLITT) stands to benefit from predictive computational modeling. The dearth of physical model parameter data leads to modeling uncertainty. This work implements a well-accepted framework with three key steps for model-building: model-parameter sensitivity analysis, model calibration, and model validation.

The sensitivity study is via generalized polynomial chaos (gPC) paired with a transient finite element (FEM) model. Uniform probability distribution functions (PDFs) capture the plausible range of values suggested by the literature for five model parameters. The five PDFs are input separately into the FEM model to gain a probabilistic sensitivity response …

Hearts And Minds: Examining The Evolution Of The Egyptian Excerebration And Evisceration Traditions Through The Impact Mummy Database, Andrew D. Wade

Electronic Thesis and Dissertation Repository

Egyptian mummification and funerary rituals were a transformative process, making the deceased a pure being; free of disease, injury, and disfigurements, as well as ethical and moral impurities. Consequently, the features of mummification available to specific categories of individuals hold social and ideological significance. This study refutes long-held classical stereotypes, particularly dogmatic class associations; demonstrates the apocryphal nature of universal heart retention; and expands on the purposes of excerebration and evisceration implied by synthetic and radiological analyses.

Features of the embalming traditions, specifically the variable excerebration and evisceration traditions, represented the Egyptian view of death. Fine-grain analyses, through primary imaging …

Patient Safety: What Can Be Done About It?, Steven Dain

Steven L Dain

Much is said and written about patient safety. In Canada, a small group of dedicated physicians, nurses and engineers participates in the Canadian Standards Association and Standards Council of Canada Advisory Committees writing basic safety and essential performance requirements for a large range of anesthesia, respiratory care and critical care equipment. Over the past several years, in recognition of the globalization of trade and the international nature of medical device design and manufacturing, Canadian Anesthesiologists’ Society members Dr Steven Dain, Dr Karen Brown, Dr Matt Kurrek, Dr Ken LeDez, and Dr Jeremy Sloan have primarily participated in Organization for International …