Biomedical Informatics Commons^™

Development, Validation, And Diagnostic Performance Of A Novel Radiomic Model For Predicting Prostate Cancer Recurrence, Linda M. Huynh

Theses & Dissertations

Multi-parametric magnetic resonance imaging (MP-MRI)-derived radiomics have been shown to capture sub-visual patterns for the quantitative characterization of prostate cancer (PC) phenotypes. The present dissertation seeks to develop, evaluate, and compare the performance of an MRI-derived radiomic model for the prediction of PC recurrence following definitive treatment with radical prostatectomy (RP).

MP-MRI was obtained from 339 patients who had a minimum of 2 years follow-up following RP at three institutions. The prostate was manually delineated as the region of interest and 924 radiomic features were extracted. All features were evaluated for stability via intraclass correlation coefficient (ICC) and image normalization …

Evidence Based Practice: A Decision-Making Guide For Health Information Professionals, Jonathan Eldredge

Faculty Book Display Case

This Guide introduces Evidence Based Practice to newcomers as well as serves as a resource for experienced practitioners. It focuses on health information professionals (informaticists, health sciences librarians, informationists, information scientists, data managers, archivists, etc.) within the US context, although others outside of the US health context might find elements of it to be valuable.

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Molecular Diagnostics - Biomarker Based Diagnosis Of Human Papillomavirus (Hpv), Lilly Hivner

Harrisburg University Research Symposium: Highlighting Research, Innovation, & Creativity

Research on how HPV-16 E6 identifies cervical cancer more often than others.

Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks

School of Medicine Faculty Publications

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML …

Self-Supervised Deep Clustering Of Single-Cell Rna-Seq Data To Hierarchically Detect Rare Cell Populations., Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen

Faculty Scholarship for the College of Science & Mathematics

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Single-Cell Gene Expression Data, Madison Dautle

Theses and Dissertations

Inferring gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data is an important computational question to reveal fundamental regulatory mechanisms. Although many computational methods have been designed to predict GRNs, none work on condition specific GRNs by directly using paired datasets of case versus control experiments, common in diverse biological research projects. We present a novel deep-learning based method, scTIGER, for GRN detection by using the co-dynamics of gene expression. scTIGER also employs cell type-based pseudotiming, an attention-based convolutional neural network method, and permutation-based significance testing to infer GRNs from gene modules. We first applied scTIGER to scRNA-seq datasets of …

A Quantitative Visualization Tool For The Assessment Of Mammographic Risky Dense Tissue Types, Margaret R. Mccarthy

Electronic Theses and Dissertations

Breast cancer is the second most occurring cancer type and is ranked fifth in terms of mortality. X-ray mammography is the most common methodology of breast imaging and can show radiographic signs of cancer, such as masses and calcifcations. From these mammograms, radiologists can also assess breast density, which is a known cancer risk factor. However, since not all dense tissue is cancer-prone, we hypothesize that dense tissue can be segregated into healthy vs. risky subtypes. We propose that risky dense tissue is associated with tissue microenvironment disorganization, which can be quantified via a computational characterization of the whole breast …

Mutation-Induced Changes In The Stability, B-Cell Epitope, And Antigenicity Of The Sars-Cov-2 Variant Spike Protein: A Comparative Computational Stud, Nira Meirita Wijayanti, Muhammad Hermawan Widyananda, Lailil Muflikhah, Nashi Widodo

Karbala International Journal of Modern Science

The spike (S) protein is a major antigenicity site that targets neutralizing antibodies and drugs. The growing number of S protein mutations has become a severe problem for developing effective vaccines. Here, we investigated four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that were the most infectious and widespread during the COVID-19 pandemic to determine the trends and patterns of mutation-induced changes in the stability, B-cell epitope, and antigenicity of the SARS-CoV-2 S protein. The data showed that the Beta and Gamma variants had three mutations on the receptor-binding domain (RBD), which is the specific site on the S …

Genome-Scale Methylation Analysis In Blood And Tumor Identifies Immune Profile, Age Acceleration, And Dna Methylation Alterations Associated With Bladder Cancer Outcomes, Ji-Qing Chen

Dartmouth College Ph.D Dissertations

Bladder cancer patients receive frequent screening due to the high tumor recurrence rate (more than 60%). Nowadays, the conventional monitoring method relies on cystoscopy which is highly invasive and increases patient morbidity and burden to the health care system with frequent follow-up. As a result, it is urgent to explore novel markers related to the outcomes of bladder cancer. Immune profiles have been associated with cancer outcomes and may have the potential to be biomarkers for outcomes management. However, little work has been conducted to investigate the associations of immune cell profiles with bladder cancer outcomes. Here, I utilized the …

Interpreting P Values In 2023, Jennifer K. Homa-Bonell

Journal of Patient-Centered Research and Reviews

If recent experiences shared among the biostatistician community are indicative of a sea change in research, then a most-welcome culture shift in dialogue surrounding the proper use and interpretation of the P value, which measures statistical probability, is underway. This editorial strives to offer guidance for researchers who would like to incorporate more comprehensive reporting in their research, namely, a broader discussion that goes beyond looking at the P value by itself and includes effect size estimates, confidence intervals, and clinical implications when interpreting quantitative results. Another evolving development in clinical research is the preferred language when referring …

Towards Generalizable Machine Learning Models For Computer-Aided Diagnosis In Medicine, Yiyang Wang

College of Computing and Digital Media Dissertations

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue …

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

McKelvey School of Engineering Theses & Dissertations

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

Classification Of Normal Versus Pneumonia From Chest X-Ray Using And Ai Model, Tassadit Lounes

Publications and Research

Hypothesis: Deep learning (DL) algorithms, in particular convolutional neural networks (CNNs), have recently been used to address a number of medical-imaging problems, such as pneumonia detection using chest X-ray, and determining the aggressiveness prostate cancer using magnetic resonance images (MRI). They have become the technique of choice in computer vision and they are the most successful type of model for image analysis.

Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang

Stratford Campus Research Day

The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the …

The Developing Effects Of Potassium Ferricyanide On Tetrahymena, Katelyn Coronell

Whittier Scholars Program

Potassium Cyanide is a highly toxic chemical asphyxiant that interferes with the body's ability to use oxygen, typically by directly affecting the body by ingestion, inhalation, skin contact, or eye contact(CDC, 2011). Due to its high toxicity, the main effect that leads to the downfall of the organism begins with the cessation of aerobic metabolism; it does this by cyanide binding to the ferric ions and inhibiting cytochrome oxidase within the mitochondria (Zhang, 2015). There are no physical dangers the substance causes. Although, there are many chemical dangers. If used at temperatures higher than 70℉ The substance may produce toxic …

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance, Kareem Wahid

Dissertations & Theses (Open Access)

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information …

Evaluation Of Medical Decision Errors During The Transition Period To Telemedicine, Marius Moroianu, Roxana- Elena Bogdan-Goroftei, Teodor Salmen, Cristina Ioana Bica, Valeria-Anca Pietrosel, Razvan Hainarosie, Anca Pantea Stoian

Journal of Mind and Medical Sciences

The context of the Coronavirus pandemic has fundamentally changed the way we approach medical services. Beyond setting up new technological possibilities, it has propelled telemedicine to become a reality, bringing undeniable practical benefits. The questions that arise are both justified and worrying: can digitalization replace a direct interpersonal relationship that involves a physical examination, while preserving the quality of the medical act and the degree of patient satisfaction? Isn't there a risk that the digitization of the medical record will cancel out the deep human character of classical medicine that has evolved since the time of Hippocrates? Should the implementation …

Meta-Narrative Review Of Possible Impacts Of Genetic Screening On Treatment Of Breast Cancer, Toqa Al Alawi, Sheza Khan, Ivey Knebel, Steven Luong, Vilma Sanchez, Kamilah Walker-Charles

Research Methods Poster Session 2023

Objective: To examine the impacts of genetic screening on the treatment of breast cancer, in relation to differences, outcomes and decisions in treatment plans or surgery in patients that performed genetic screening versus those that did not.

Background: Genetic screening technology has become commercially available, yet standard preventative care for breast cancer has no genetic screening involved. Genetic screening in breast cancer treatment is performed, but its usage is not standardized.

Methods: Findings were synthesized using the meta-narrative review style to examine articles retrieved from searches of digital databases PubMed and the M.D. Anderson Scholarly Library.

Discussion: Articles were selected …

Mapping Next Generation Sequence Data With Bwa (Burrows-Wheel Aligner) On Galaxy Software, Rabeh Z. Omar

Honors College Theses

Advancement of next-generation sequencing technologies introduces a vast amount of data which has become a challenge for researchers to organize and sequence data sets. BWA (Burrows-Wheeler Aligner) is one of the widely used software for aligning and mapping sequencing data against a reference genome. In my thesis, I present a comprehensive guide for analyzing genome sequences using BWA. I discuss the various steps involved in the process, including gathering the data, preparing the reference genome, aligning the sequences, and processing the data to visualize the results.