Data Science Commons^™

Social And Behavior Factors Of Alzheimer's Disease And Related Dementias: A National Study In The Us, David Ciciora, Elizabeth Vásquez, Edward Valachovic, Lifang Hou, Yinan Zheng, Hua Xu, Xiaoqian Jiang, Kun Huang, Kelley Pettee Gabriel, Hong-Wen Deng, Mary P Gallant, Kai Zhang

Faculty, Staff and Student Publications

Introduction: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated.

Methods: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by …

Digital Health Technologies For High-Risk Pregnancy Management: Three Case Studies Using Digilego Framework, Sahiti Myneni, Alexandra Zingg, Tavleen Singh, Angela Ross, Amy Franklin, Deevakar Rogith, Jerrie Refuerzo

Faculty, Staff and Student Publications

OBJECTIVE: High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.

METHODS: We describe three studies that leverage core methods from

RESULTS: Scalable social computing models using deep learning classifiers …

Learning From Data In Dentistry: Summary Of The Third Annual Openwide Conference, Elsbeth Kalenderian, Kawtar Zouaidi, Jan Yeager, Janelle Urata, Alfa Yansane, Bunmi Tokede, D Brad Rindal, Heiko Spallek, Joel White, Muhammad Walji

Faculty, Staff and Student Publications

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) …

High Dietary Folic Acid Supplementation Reduced The Composition Of Fatty Acids And Amino Acids In Fortified Eggs, Ao-Chuan Yu, Yu-Han Deng, Cheng Long, Xi-Hui Sheng, Xiang-Guo Wang, Long-Fei Xiao, Xue-Ze Lv, Xiang-Ning Chen, Li Chen, Xiao-Long Qi

Faculty, Staff and Student Publications

AIMS: The study aimed to evaluate the effects of dietary folic acid (FA) on the production performance of laying hens, egg quality, and the nutritional differences between eggs fortified with FA and ordinary eggs.

METHODS: A total of 288 26-week-old Hy-Line Brown laying hens (initial body weights 1.65 ± 0.10 kg) with a similar weight and genetic background were used. A completely randomized design divided the birds into a control group and three treatment groups. Each group consisted of six replicates, with twelve chickens per replicate. Initially, all birds were fed a basal diet for 1 week. Subsequently, they were …

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …

A Self-Supervised Learning Approach For Registration Agnostic Imaging Models With 3d Brain Cta, Yingjun Dong, Samiksha Pachade, Xiaomin Liang, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

Deep learning-based neuroimaging pipelines for acute stroke typically rely on image registration, which not only increases computation but also introduces a point of failure. In this paper, we propose a general-purpose contrastive self-supervised learning method that converts a convolutional deep neural network designed for registered images to work on a different input domain, i.e., with unregistered images. This is accomplished by using a self-supervised strategy that does not rely on labels, where the original model acts as a teacher and a new network as a student. Large vessel occlusion (LVO) detection experiments using computed tomographic angiography (CTA) data from 402 …

Label-Aware Distance Mitigates Temporal And Spatial Variability For Clustering And Visualization Of Single-Cell Gene Expression Data, Shaoheng Liang, Jinzhuang Dou, Ramiz Iqbal, Ken Chen

Faculty, Staff and Student Publications

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our …

An Exposome Atlas Of Serum Reveals The Risk Of Chronic Diseases In The Chinese Population, Lei You, Jing Kou, Mengdie Wang, Guoqin Ji, Xiang Li, Chang Su, Fujian Zheng, Mingye Zhang, Yuting Wang, Tiantian Chen, Ting Li, Lina Zhou, Xianzhe Shi, Chunxia Zhao, Xinyu Liu, Surong Mei, Guowang Xu

Faculty, Staff and Student Publications

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, …

The Acceptance And Use Of Digital Technologies For Self-Reporting Medication Safety Events After Care Transitions To Home In Patients With Cancer: Survey Study, Yun Jiang, Misun Hwang, Youmin Cho, Christopher R Friese, Sarah T Hawley, Milisa Manojlovich, John C Krauss, Yang Gong

Faculty, Staff and Student Publications

BACKGROUND: Actively engaging patients with cancer and their families in monitoring and reporting medication safety events during care transitions is indispensable for achieving optimal patient safety outcomes. However, existing patient self-reporting systems often cannot address patients' various experiences and concerns regarding medication safety over time. In addition, these systems are usually not designed for patients' just-in-time reporting. There is a significant knowledge gap in understanding the nature, scope, and causes of medication safety events after patients' transition back home because of a lack of patient engagement in self-monitoring and reporting of safety events. The challenges for patients with cancer in …

Characterization Of Biological Particles Using An Integrated Hyperspectral Imaging And Machine Learning, Kaeul Lim, Arezoo Ardekani

Graduate Industrial Research Symposium

Hyperspectral imaging (HSI) is a promising modality in medicine with many potential applications. This study focuses on developing a label-free lipid nanoparticle characterization method using a convolutional neural network (CNN) analysis of HSI images. The HSI data, hypercube, consists of a series of images acquired at different wavelengths for the same field of view, providing continuous spectra information for each pixel. Three distinct liposome samples were collected for analysis. Advanced image preprocessing and classification methods for HSI data were developed to differentiate liposomes based on their material compositions. Our machine learning-based classification method was able to distinguish different liposome types …

Sepsis Treatment: Reinforced Sequential Decision-Making For Saving Lives, Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Graduate Industrial Research Symposium

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications. Existing machine learning methods for managing sepsis struggle in offline scenarios, exhibiting suboptimal performance with survival rates below 50%. Our project introduces the "PosNegDM: Reinforcement Learning with Positive and Negative Demonstrations for Sequential Decision-Making" framework utilizing an innovative transformer-based model and a feedback reinforcer to replicate expert actions while considering individual patient characteristics. A mortality classifier with 96.7% accuracy guides treatment decisions towards positive outcomes. The PosNegDM framework significantly improves patient survival, saving 97.39% of patients and outperforming established machine learning …

Assessing Gait Metrics For Early Parkinson's Disease Prediction: A Preliminary Analysis Of Underfit Models, Daniel Salinas, Gerardo Medellin, Katherine Bolado, Tomas Gomez, Kelsey Potter-Baker, Nawaz Khan Abdul Hack, Ramu Vadukapuram

Research Symposium

Background: Parkinson's Disease (PD) is characterized by both motor and non-motor symptoms, and its diagnosis primarily relies on clinical presentation. There is a growing need for diagnostic tools to identify the early signs of PD, particularly the initial motor impairments often manifested as gait abnormalities. Here we seek to present preliminary findings to address this need. Our study focuses on using Machine Learning techniques (ML) to predict the PD clinical stage most efficiently and accurately. Specifically, we have sought to evaluate how spatiotemporal characteristics and other locomotor performance variables obtained on a walkway system can be utilized to identify the …

Deep Learning Model For Personalized Prediction Of Positive Mrsa Culture Using Time-Series Electronic Health Records, Masayuki Nigo, Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie, Degui Zhi

Faculty, Staff and Student Publications

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC

Spec: A Soft Prompt-Based Calibration On Performance Variability Of Large Language Model In Clinical Notes Summarization, Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also …

Multiple Control Of Azoquinoline Based Molecular Photoswitches, Youming Lv, Hebo Ye, Lei You

Faculty, Staff and Student Publications

Multi-addressable molecular switches with high sophistication are creating intensive interest, but are challenging to control. Herein, we incorporated ring-chain dynamic covalent sites into azoquinoline scaffolds for the construction of multi-responsive and multi-state switching systems. The manipulation of ring-chain equilibrium by acid/base and dynamic covalent reactions with primary/secondary amines allowed the regulation of

The Pathogenicity Of Vancomycin-Resistant Enterococcus Faecalis To Colon Cancer Cells, Li Zhang, Mingxia Deng, Jing Liu, Jiajie Zhang, Fangyu Wang, Wei Yu

Faculty, Staff and Student Publications

BACKGROUND: The aim of this study was to investigate the pathogenicity of vancomycin-resistant Enterococcus faecalis (VREs) to human colon cells in vitro.

METHODS: Three E. faecalis isolates (2 VREs and E. faecalis ATCC 29212) were cocultured with NCM460, HT-29 and HCT116 cells. Changes in cell morphology and bacterial adhesion were assessed at different time points. Interleukin-8 (IL-8) and vascular endothelial growth factor A (VEGFA) expression were measured via RT-qPCR and enzyme-linked immunosorbent assay (ELISA), respectively. Cell migration and human umbilical vein endothelial cells (HUVECs) tube formation assays were used for angiogenesis studies. The activity of PI3K/AKT/mTOR signaling pathway was measured …

Cancergpt For Few Shot Drug Pair Synergy Prediction Using Large Pretrained Language Models, Tianhao Li, Sandesh Shetty, Advaith Kamath, Ajay Jaiswal, Xiaoqian Jiang, Ying Ding, Yejin Kim

Faculty, Staff and Student Publications

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. …

Mortality Outcomes In A Large Population With And Without Covert Cerebrovascular Disease, Úna Clancy, Eric J Puttock, Wansu Chen, William Whiteley, Ellen M Vickery, Lester Y Leung, Patrick H Luetmer, David F Kallmes, Sunyang Fu, Chengyi Zheng, Hongfang Liu, David M Kent

Faculty, Staff and Student Publications

Covert cerebrovascular disease (CCD) is frequently reported on neuroimaging and associates with increased dementia and stroke risk. We aimed to determine how incidentally-discovered CCD during clinical neuroimaging in a large population associates with mortality. We screened CT and MRI reports of adults aged ≥50 in the Kaiser Permanente Southern California health system who underwent neuroimaging for a non-stroke clinical indication from 2009-2019. Natural language processing identified incidental covert brain infarcts (CBI) and/or white matter hyperintensities (WMH), grading WMH as mild/moderate/severe. Models adjusted for age, sex, ethnicity, multimorbidity, vascular risks, depression, exercise, and imaging modality. Of n=241,028, the mean age was …

Strategies To Combine 3d Vasculature And Brain Cta With Deep Neural Networks: Application To Lvo, Uma M Lal-Trehan Estrada, Arnau Oliver, Sunil A Sheth, Xavier Lladó, Luca Giancardo

Faculty, Staff and Student Publications

Automated tools to detect large vessel occlusion (LVO) in acute ischemic stroke patients using brain computed tomography angiography (CTA) have been shown to reduce the time for treatment, leading to better clinical outcomes. There is a lot of information in a single CTA and deep learning models do not have an obvious way of being conditioned on areas most relevant for LVO detection, i.e., the vasculature structure. In this work, we compare and contrast strategies to make convolutional neural networks focus on the vasculature without discarding context information of the brain parenchyma and propose an attention-inspired strategy to encourage this. …

Generalizable Pipeline For Constructing Hiv Risk Prediction Models Across Electronic Health Record Systems, Sarah B May, Thomas P Giordano, Assaf Gottlieb

Faculty, Staff and Student Publications

OBJECTIVE: The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk. Existing HIV risk prediction algorithms rely on manual construction of features and are limited in their application across diverse electronic health record systems. Furthermore, the accuracy of these models in predicting HIV in females has thus far been …

The Il6/Jak/Stat3 Signaling Axis Is A Therapeutic Vulnerability In Smarcb1-Deficient Bladder Cancer, Chandra Sekhar Amara, Karthik Reddy Kami Reddy, Yang Yuntao, Yuen San Chan, Danthasinghe Waduge Badrajee Piyarathna, Lacey Elizabeth Dobrolecki, David J H Shih, Zhongcheng Shi, Jun Xu, Shixia Huang, Matthew J Ellis, Andrea B Apolo, Leomar Y Ballester, Jianjun Gao, Donna E Hansel, Yair Lotan, H Courtney Hodges, Seth P Lerner, Chad J Creighton, Arun Sreekumar, W Jim Zheng, Pavlos Msaouel, Shyam M Kavuri, Nagireddy Putluri

Faculty, Staff and Student Publications

SMARCB1 loss has long been observed in many solid tumors. However, there is a need to elucidate targetable pathways driving growth and metastasis in SMARCB1-deficient tumors. Here, we demonstrate that SMARCB1 deficiency, defined as genomic SMARCB1 copy number loss associated with reduced mRNA, drives disease progression in patients with bladder cancer by engaging STAT3. SMARCB1 loss increases the chromatin accessibility of the STAT3 locus in vitro. Orthotopically implanted SMARCB1 knockout (KO) cell lines exhibit increased tumor growth and metastasis. SMARCB1-deficient tumors show an increased IL6/JAK/STAT3 signaling axis in in vivo models and patients. Furthermore, a pSTAT3 selective inhibitor, TTI-101, reduces …

Sox On Tumors, A Comfort Or A Constraint?, Junqing Jiang, Yufei Wang, Mengyu Sun, Xiangyuan Luo, Zerui Zhang, Yijun Wang, Siwen Li, Dian Hu, Jiaqian Zhang, Zhangfan Wu, Xiaoping Chen, Bixiang Zhang, Xiao Xu, Shuai Wang, Shengjun Xu, Wenjie Huang, Limin Xia

Faculty, Staff and Student Publications

The sex-determining region Y (SRY)-related high-mobility group (HMG) box (SOX) family, composed of 20 transcription factors, is a conserved family with a highly homologous HMG domain. Due to their crucial role in determining cell fate, the dysregulation of SOX family members is closely associated with tumorigenesis, including tumor invasion, metastasis, proliferation, apoptosis, epithelial-mesenchymal transition, stemness and drug resistance. Despite considerable research to investigate the mechanisms and functions of the SOX family, confusion remains regarding aspects such as the role of the SOX family in tumor immune microenvironment (TIME) and contradictory impacts the SOX family exerts on tumors. This review summarizes …

Transcription Factor Bach1 In Cancer: Roles, Mechanisms, And Prospects For Targeted Therapy, Dian Hu, Zerui Zhang, Xiangyuan Luo, Siwen Li, Junqing Jiang, Jiaqian Zhang, Zhangfan Wu, Yijun Wang, Mengyu Sun, Xiaoping Chen, Bixiang Zhang, Xiao Xu, Shuai Wang, Shengjun Xu, Yufei Wang, Wenjie Huang, Limin Xia

Faculty, Staff and Student Publications

Transcription factor BTB domain and CNC homology 1 (BACH1) belongs to the Cap 'n' Collar and basic region Leucine Zipper (CNC-bZIP) family. BACH1 is widely expressed in mammalian tissues, where it regulates epigenetic modifications, heme homeostasis, and oxidative stress. Additionally, it is involved in immune system development. More importantly, BACH1 is highly expressed in and plays a key role in numerous malignant tumors, affecting cellular metabolism, tumor invasion and metastasis, proliferation, different cell death pathways, drug resistance, and the tumor microenvironment. However, few articles systematically summarized the roles of BACH1 in cancer. This review aims to highlight the research status …

Patient And Dentist Perspectives On Collecting Patient Reported Outcomes After Painful Dental Procedures In The National Dental Pbrn, Elsbeth Kalenderian, Sayali Tungare, Urvi Mehta, Sharmeen Hamid, Rahma Mungia, Alfa-Ibrahim Yansane, David Holmes, Kim Funkhouser, Ana M Ibarra-Noriega, Janelle Urata, D Brad Rindal, Heiko Spallek, Joel White, Muhammad F Walji

Faculty, Staff and Student Publications

BACKGROUND: Dental Patient Reported Outcomes (PROs) relate to a dental patient's subjective experience of their oral health. How practitioners and patients value PROs influences their successful use in practice.

METHODS: Semi-structured interviews were conducted with 22 practitioners and 32 patients who provided feedback on using a mobile health (mHealth) platform to collect the pain experience after dental procedures. A themes analysis was conducted to identify implementation barriers and facilitators.

RESULTS: Five themes were uncovered: (1) Sense of Better Care. (2) Tailored Follow-up based on the dental procedure and patient's pain experience. (3) Effective Messaging and Alerts. (4) Usable Digital Platform. …

The Hsp90-Myc-Cdk9 Network Drives Therapeutic Resistance In Mantle Cell Lymphoma, Fangfang Yan, Vivian Jiang, Alexa Jordan, Yuxuan Che, Yang Liu, Qingsong Cai, Yu Xue, Yijing Li, Joseph Mcintosh, Zhihong Chen, Jovanny Vargas, Lei Nie, Yixin Yao, Heng-Huan Lee, Wei Wang, Johnnelson R Bigcal, Maria Badillo, Jitendra Meena, Christopher Flowers, Jia Zhou, Zhongming Zhao, Lukas M Simon, Michael Wang

Faculty, Staff and Student Publications

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 …

Dynamic Prognosis Prediction For Patients On Dapt After Drug-Eluting Stent Implantation: Model Development And Validation, Fang Li, Laila Rasmy, Yang Xiang, Jingna Feng, Ahmed Abdelhameed, Xinyue Hu, Zenan Sun, David Aguilar, Abhijeet Dhoble, Jingcheng Du, Qing Wang, Shuteng Niu, Yifang Dang, Xinyuan Zhang, Ziqian Xie, Yi Nian, Jianping He, Yujia Zhou, Jianfu Li, Mattia Prosperi, Jiang Bian, Degui Zhi, Cui Tao

Faculty, Staff and Student Publications

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management.

METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were …

Real-World Trends, Rural-Urban Differences, And Socioeconomic Disparities In Utilization Of Narrow Versus Broad Next-Generation Sequencing Panels, Yiqing Zhao, Anastasios Dimou, Zachary C Fogarty, Jun Jiang, Hongfang Liu, William B Wong, Chen Wang

Faculty, Staff and Student Publications

UNLABELLED: Advances in genetic technology have led to the increasing use of genomic panels in precision oncology practice, with panels ranging from a couple to hundreds of genes. However, the clinical utilization and utility of oncology genomic panels, especially among vulnerable populations, is unclear. We examined the association of panel size with socioeconomic status and clinical trial matching. We retrospectively identified 9,886 eligible adult subjects in the Mayo Clinic Health System who underwent genomic testing between January 1, 2016 and June 30, 2020. Patient data were retrieved from structured and unstructured data sources of institutional collections, including cancer registries, clinical …

Identification Of New Egfr Inhibitors By Structure-Based Virtual Screening And Biological Evaluation, Shuyi Wang, Xiaotian Xu, Chuxin Pan, Qian Guo, Qinlan Li, Shanhe Wan, Zhonghuang Li, Jiajie Zhang, Xiaoyun Wu

Faculty, Staff and Student Publications

Epidermal growth factor receptor (EGFR) inhibitors have been used in clinical for the treatment of non-small-cell lung cancer for years. However, the emergence of drug resistance continues to be a major problem. To identify potential inhibitors, molecular docking-based virtual screening was conducted on ChemDiv and Enamine commercial databases using the Glide program. After multi-step VS and visual inspection, a total of 23 compounds with novel and varied structures were selected, and the predicted ADMET properties were within the satisfactory range. Further molecular dynamics simulations revealed that the reprehensive compound ZINC49691377 formed a stable complex with the allosteric pocket of EGFR …

Epigenetic Regulation In Cancer, Minzhi Gu, Bo Ren, Yuan Fang, Jie Ren, Xiaohong Liu, Xing Wang, Feihan Zhou, Ruiling Xiao, Xiyuan Luo, Lei You, Yupei Zhao

Faculty, Staff and Student Publications

Epigenetic modifications are defined as heritable changes in gene activity that do not involve changes in the underlying DNA sequence. The oncogenic process is driven by the accumulation of alterations that impact genome's structure and function. Genetic mutations, which directly disrupt the DNA sequence, are complemented by epigenetic modifications that modulate gene expression, thereby facilitating the acquisition of malignant characteristics. Principals among these epigenetic changes are shifts in DNA methylation and histone mark patterns, which promote tumor development and metastasis. Notably, the reversible nature of epigenetic alterations, as opposed to the permanence of genetic changes, positions the epigenetic machinery as …

Clustering Of Patients With Heart Disease, Mukadder Cinar

Dissertations, Theses, and Capstone Projects

Heart disease, a leading cause of mortality worldwide, presents complex challenges in public health due to its varied manifestations. Accurate diagnosis and patient stratification are essential for effective management and improved outcomes. In response, this study employed machine learning techniques to analyze heart disease data obtained from UCI Machine Learning Repository, aiming to enhance patient care through advanced data analysis.

The study began with the application of K-Nearest Neighbors (KNN) classification, which categorized patients into 'Disease' and 'No Disease' groups. This preliminary step provided initial insights into the structure of the dataset. Subsequently, K-means clustering was applied in two rounds, …