Data Science Commons^™

Interpretation Models For Prostate Lesion: Detecting, Explaining, And Understanding., Mehmet Akif Gulum

Electronic Theses and Dissertations

Prostate cancer is a major public health concern, affecting millions of men worldwide. While early detection and treatment of prostate cancer is critical for improving patient outcomes, the detection of prostate lesions is even more important for timely intervention and management of the disease. Prostate lesions are abnormal growths or lumps within the prostate gland, which may or may not be cancerous. The timely detection and accurate diagnosis of prostate lesions is crucial for effective treatment and management of the disease. In recent years, deep learning models have shown promise in accurately detecting and characterizing prostate lesions using advanced imaging …

Offensive Content Detection In Online Social Platforms, Ebuka Okpala

All Dissertations

Online social platforms enable users to connect with large, diverse audiences and the ability for a message or content to flow from one user to another user, user to followers, followers to user, and followers to followers. Of course, the advantages of this are apparent, and the dangers are also clearly obvious. The user-generated content could be abusive, offensive, or hateful to other users, possibly leading to adverse health effects or offline harm. As more of society's public discourse and interaction move online and these platforms grow and increase their reach, it is inherently important to protect the safety of …

Influential Factors And Predicting Dose Delivery Accuracy For Imaging And Radiation Oncology Core’S Phantom Program Using Machine Learning, Hunter Mehrens

Dissertations and Theses (Open Access)

IROC’s mission is to help ensure consistent and comparable, high-quality radiotherapy across clinics that participate in national clinical trials. To obtain this mission, IROC’s phantom program provides a third-party end-to-end check of the clinical workflow of a patient receiving radiotherapy. The goal of the phantom audit is to compare the dose delivered to the dose planned by the treatment system ensuring dose delivery accuracy. While IROC’s phantoms are better equipped to catch dose delivery errors compared to a clinic’s QA process, the end-to-end process and reporting of results is time-consuming creating a bottleneck for clinical trial participation. Furthermore, IROC’s passing …

Physics-Informed Machine Learning Methods For Inverse Design Of Multi-Phase Materials With Targeted Mechanical Properties, Yunpeng Wu

All Dissertations

Advances in machine learning algorithms and applications have significantly enhanced engineering inverse design capabilities. This work focuses on the machine learning-based inverse design of material microstructures with targeted linear and nonlinear mechanical properties. It involves developing and applying predictive and generative physics-informed neural networks for both 2D and 3D multiphase materials.

The first investigation aims to develop a machine learning method for the inverse design of 2D multiphase materials, particularly porous materials. We first develop machine learning methods to understand the implicit relationship between a material's microstructure and its mechanical behavior. Specifically, we use ResNet-based models to predict the elastic …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Book Review: How To Expect The Unexpected: The Science Of Making Predictions -- And The Art Of Knowing When Not To By Kit Yates, Mark Huber

Journal of Humanistic Mathematics

Humans think about the future all the time. Prediction is a part of how we prepare for the coming of both good and bad events in our lives. Kit Yates' book, How to expect the unexpected, concentrates primarily on the question of why prediction is difficult, and what mental shortcuts people take in prediction that can lead to incorrect results. Unfortunately, a lack of concern for details and several omissions undermine the quality of the book.

Safer: Sub-Hypergraph Attention-Based Neural Network For Predicting Effective Responses To Dose Combinations, Yi-Ching Tang, Rongbin Li, Jing Tang, W Jim Zheng, Xiaoqian Jiang

Faculty, Staff and Student Publications

BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods.

RESULTS: We introduce …

Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola

Journal of Aviation Technology and Engineering

This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In …

Review Of Queer Data Studies, Jordan Meyerl

Journal of Contemporary Archival Studies

In Queer Data Studies, editor Patrick Keilty compiles essays from scholars and practitioners exploring the relationship between data and queer subjects. Utilizing a cross-disciplinary approach, the volume encourages readers to rethink what constitutes queer data and how queer subjects choose to interact with a world where surveillance is increasingly regarded as the norm. This review provides readers with an introduction to the book’s 10 chapters, while also evaluating its strengths and weaknesses and highlighting avenues for future research in this budding field.

The Significant Role Of Amino Acid Metabolic Reprogramming In Cancer, Xiaohong Liu, Bo Ren, Jie Ren, Minzhi Gu, Lei You, Yupei Zhao

Faculty, Staff and Student Publications

Amino acid metabolism plays a pivotal role in tumor microenvironment, influencing various aspects of cancer progression. The metabolic reprogramming of amino acids in tumor cells is intricately linked to protein synthesis, nucleotide synthesis, modulation of signaling pathways, regulation of tumor cell metabolism, maintenance of oxidative stress homeostasis, and epigenetic modifications. Furthermore, the dysregulation of amino acid metabolism also impacts tumor microenvironment and tumor immunity. Amino acids can act as signaling molecules that modulate immune cell function and immune tolerance within the tumor microenvironment, reshaping the anti-tumor immune response and promoting immune evasion by cancer cells. Moreover, amino acid metabolism can …

Real-World Effectiveness And Tolerability Of Interferon-Free Direct-Acting Antiviral For 15,849 Patients With Chronic Hepatitis C: A Multinational Cohort Study, Fanpu Ji, Sally Tran, Eiichi Ogawa, Chung-Feng Huang, Takanori Suzuki, Yu Jun Wong, Hidenori Toyoda, Dae Won Jun, Liu Li, Haruki Uojima, Akito Nozaki, Makoto Chuma, Cheng-Hao Tseng, Yao-Chun Hsu, Masatoshi Ishigami, Takashi Honda, Masanori Atsukawa, Hiroaki Haga, Masaru Enomoto, Huy Trinh, Carmen Monica Preda, Phillip Vutien, Charles Landis, Dong Hyun Lee, Tsunamasa Watanabe, Hirokazu Takahashi, Hiroshi Abe, Akira Asai, Yuichiro Eguchi, Jie Li, Xiaozhong Wang, Jia Li, Junping Liu, Jing Liang, Carla Pui-Mei Lam, Rui Huang, Qing Ye, Hongying Pan, Jiajie Zhang, Dachuan Cai, Qi Wang, Daniel Q Huang, Grace Wong, Vincent Wai-Sun Wong, Junyi Li, Son Do, Norihiro Furusyo, Makoto Nakamuta, Hideyuki Nomura, Eiji Kajiwara, Eileen L Yoon, Sang Bong Ahn, Koichi Azuma, Kazufumi Dohmen, Jihyun An, Do Seon Song, Hyun Chin Cho, Akira Kawano, Toshimasa Koyanagi, Aritsune Ooho, Takeaki Satoh, Kazuhiro Takahashi, Ming-Lun Yeh, Pei-Chien Tsai, Satoshi Yasuda, Yunyu Zhao, Yishan Liu, Tomomi Okubo, Norio Itokawa, Mi Jung Jun, Toru Ishikawa, Koichi Takaguchi, Tomonori Senoh, Mingyuan Zhang, Changqing Zhao, Raluca Ioana Alecu, Wei Xuan Tay, Pooja Devan, Joanne Kimiko Liu, Ritsuzo Kozuka, Elena Vargas-Accarino, Ai-Thien Do, Mayumi Maeda, Wan-Long Chuang, Jee-Fu Huang, Chia-Yen Dai, Ramsey Cheung, Maria Buti, Junqi Niu, Wen Xie, Hong Ren, Seng Gee Lim, Chao Wu, Man-Fung Yuen, Jia Shang, Qiang Zhu, Yoshiyuki Ueno, Yasuhito Tanaka, Jun Hayashi, Ming-Lung Yu, Mindie H Nguyen

Faculty, Staff and Student Publications

BACKGROUND AND AIMS: As practice patterns and hepatitis C virus (HCV) genotypes (GT) vary geographically, a global real-world study from both East and West covering all GTs can help inform practice policy toward the 2030 HCV elimination goal. This study aimed to assess the effectiveness and tolerability of DAA treatment in routine clinical practice in a multinational cohort for patients infected with all HCV GTs, focusing on GT3 and GT6.

METHODS: We analyzed the sustained virological response (SVR12) of 15,849 chronic hepatitis C patients from 39 Real-World Evidence from the Asia Liver Consortium for HCV clinical sites in Asia Pacific, …

Identifying The O’Connell Effect In Eclipsing Binary Stars, Nicholas Paolella

Computer Science and Information Technology Faculty

Data science techniques have wide-ranging applications throughout scientific explorations. One, is filtering astronomical data to better understand specific populations, such as binary stars. Specifically, binary stars that exhibit the O’Connell effect are worthy of study as this phenomenon is still not well understood. The O’Connell effect can be defined as the asymmetry of maxima in the light curves, as captured by the instrument, while observing the eclipsing binary system in question. There is significant data captured by NASA and curated by Villanova University, which enabled the investigation of eclipsing binary stars and the attributes of which may help identify the …

Effect Of Esketamine On Hypotension In Women With Preoperative Anxiety Undergoing Elective Cesarean Section: A Randomized, Double-Blind, Controlled Trial, Yu Qi, Meiyan Zhou, Yaqi Dong, Wenting Zheng, Qinyu Jiang, Yanyu Li, Xinghe Wang, Jia Sun, Hai Zhou, Zhengquan Hu, Liwei Wang

Faculty, Staff and Student Publications

To investigate the effect of low-doses esketamine on spinal anesthesia-induced hypotension in women with preoperative anxiety undergoing elective cesarean section, the randomized controlled trial enrolled 120 women aged 18-35 years who preoperative State-Trait Anxiety Inventory State scores > 40, conducted from September 2022 to August 2023 in Xuzhou Central Hospital, China. Women in the esketamine group received a single intravenous injection of 0.2 mg/kg esketamine after sensory block level achieved. The incidence of hypotension in the esketamine group was significantly lower than the control group at T2 (10% [6 of 60]; P < 0.001), T3 (5.0% [3 of 60]; P = 0.007) and T4(5.0% [3 of 60]; P = 0.004). Despite being higher in the esketamine group, the overall rates of hypertension (11.7% [7 of 60]; P = 0.186), tachycardia (23.3% [14 of 60]; P = 0.246), and bradycardia (0.0% [0 of 60]; P = 0.079) were no significantly difference between the two groups. STAI-S scores was significantly lower in the esketamine group (mean [SD] 37.52[7.14]) than in the control group (mean [SD] 41.03[9.66], P = 0.39) in postoperative day 1. Spinal anesthesia combined with intravenous low-doses esketamine infusion can significantly reduce the incidence of hypotension in women with preoperative anxiety undergoing elective cesarean section.

Low Skeletal Muscle Mass Is Associated With Inferior Preoperative And Postoperative Shoulder Function In Elderly Rotator Cuff Tear Patients, Yang Yang, Binbin Zheng, Xiaofang Lin, Mengqin Zhang, Yongzhi Ye, Haixiao Chen, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: The age-related loss of skeletal muscle mass is an important characteristic of sarcopenia, an increasingly recognized condition with systemic implications. However, its association with shoulder function in elderly patients with rotator cuff tears (RCT) remains unknown. This study aimed to investigate the relationship between low skeletal muscle mass and shoulder function in elderly RCT patients.

METHODS: A retrospective analysis was conducted on RCT patients who underwent chest computed tomography (CT) scans for clinical evaluation. Preoperative CT scan images of the chest were used to calculate the cross-sectional area (CSA) of thoracic muscle at the T4 level. The medical records …

Innovation Path At Institute For Protein Design Of Washington University And Its Enlightenment For Construction Of New Life Sciences R&D Institutions, Runzhou Zhao, Ming Ni, Yunzhi Fa, Xiaochen Bo, Jian Jiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The Institute for Protein Design (IPD) at the University of Washington is a pioneering local and state-supported non-profit scientific research institution. Since its establishment in 2012, IPD has seized the opportunity of AI for Science and open science, and continuously enhanced its capabilities of fundamental innovations, breakthrough technologies, and industrial impact. We summarized five factors contributing to IPD’s development, including focusing on the cutting-edge issues of basic scientific research to gain a first-mover advantage and then further expand, integrating AI-enhanced digital tools and solid experimental validations, facilitating the integrated development of innovation and industrial chains, giving full play to the …

Deepface: Deep-Learning-Based Framework To Contextualize Orofacial-Cleft-Related Variants During Human Embryonic Craniofacial Development, Yulin Dai, Toshiyuki Itai, Guangsheng Pei, Fangfang Yan, Yan Chu, Xiaoqian Jiang, Seth M Weinberg, Nandita Mukhopadhyay, Mary L Marazita, Lukas M Simon, Peilin Jia, Zhongming Zhao

Faculty, Staff and Student Publications

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between …

Prediction Of Mild Cognitive Impairment Status: Pilot Study Of Machine Learning Models Based On Longitudinal Data From Fitness Trackers, Qidi Xu, Yejin Kim, Karen Chung, Paul Schulz, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI).

OBJECTIVE: This study aimed to use data collected from fitness trackers to predict MCI status.

METHODS: In this pilot study, fitness trackers were …

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.

Epigenome-Wide Association Study Of Lung Cancer Among Never Smokers In Two Prospective Cohorts In Shanghai, China, Mohammad L Rahman, Charles E Breeze, Xiao-Ou Shu, Jason Y Y Wong, Batel Blechter, Andres Cardenas, Xuting Wang, Bu-Tian Ji, Wei Hu, Qiuyin Cai, H Dean Hosgood, Gong Yang, Jianxin Shi, Jirong Long, Yu-Tang Gao, Douglas A Bell, Wei Zheng, Nathaniel Rothman, Qing Lan

Faculty, Staff and Student Publications

BACKGROUND: The aetiology of lung cancer among individuals who never smoked remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Epigenetic alterations, particularly DNA methylation (DNAm) changes, have emerged as potential drivers. Yet, few prospective epigenome-wide association studies (EWAS), primarily focusing on peripheral blood DNAm with limited representation of never smokers, have been conducted.

METHODS: We conducted a nested case-control study of 80 never-smoking incident lung cancer cases and 83 never-smoking controls within the Shanghai Women's Health Study and Shanghai Men's Health Study. DNAm was measured in prediagnostic oral rinse …

Riesz Particle Markov Chain Monte Carlo Methods, Xiongming Dai

LSU Doctoral Dissertations

Markov chain Monte Carlo (MCMC) methods are simulations that explore complex statistical distributions, while bypassing the cumbersome requirement of a specific analytical expression for the target. This stochastic exploration of an uncertain parameter space comes at the expense of a large number of ``burn-in'' samples, and the computational complexity leads to the curse of dimensionality. Although at the exploration level, some methods have been proposed to accelerate the convergence of the algorithm, such as tempering, Hamiltonian Monte Carlo, Rao-redwellization, and scalable methods for better performance, they cannot avoid the stochastic nature of this exploration. We develop algorithms for the energy …

A Comparative Analysis Of Shap, Lime, Anchors, And Dice For Interpreting A Dense Neural Network In Credit Card Fraud Detection, Bujar Raufi, Ciaran Finnegan, Luca Longo

Conference papers

Financial institutions heavily rely on advanced Machine Learning algorithms to screen transactions. However, they face increasing pressure from regulators and the public to ensure AI accountability and transparency, particularly in credit card fraud detection. While ML technology has effectively detected fraudulent activity, the opacity of Artificial Neural Networks (ANN) can make it challenging to explain decisions. This has prompted a recent push for more explainable fraud prevention tools. Although vendors claim to improve detection rates, integrating explanation data is still early. Data scientists recognize the potential of Explainable AI (XAI) techniques in fraud prevention, but comparative research on their effectiveness …

Context-Dependent T-Box Transcription Factor Family: From Biology To Targeted Therapy, Siwen Li, Xiangyuan Luo, Mengyu Sun, Yijun Wang, Zerui Zhang, Junqing Jiang, Dian Hu, Jiaqian Zhang, Zhangfan Wu, Yufei Wang, Wenjie Huang, Limin Xia

Faculty, Staff and Student Publications

T-BOX factors belong to an evolutionarily conserved family of transcription factors. T-BOX factors not only play key roles in growth and development but are also involved in immunity, cancer initiation, and progression. Moreover, the same T-BOX molecule exhibits different or even opposite effects in various developmental processes and tumor microenvironments. Understanding the multiple roles of context-dependent T-BOX factors in malignancies is vital for uncovering the potential of T-BOX-targeted cancer therapy. We summarize the physiological roles of T-BOX factors in different developmental processes and their pathological roles observed when their expression is dysregulated. We also discuss their regulatory roles in tumor …

The Protective Efficacy Of A Sars-Cov-2 Vaccine Candidate B1351v Against Several Variant Challenges In K18-Hace2 Mice, Jie Yang, Huifen Fan, Anna Yang, Wenhui Wang, Xin Wan, Fengjie Lin, Dongsheng Yang, Jie Wu, Kaiwen Wang, Wei Li, Qian Cai, Lei You, Deqin Pang, Jia Lu, Changfu Guo, Jinrong Shi, Yan Sun, Xinguo Li, Kai Duan, Shuo Shen, Shengli Meng, Jing Guo, Zejun Wang

Faculty, Staff and Student Publications

The emergence of SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and partial resistance to neutralization by antibodies has been observed globally. There is an urgent need for an effective vaccine to combat these variants. Our study demonstrated that the B.1.351 variant inactivated vaccine candidate (B.1.351V) generated strong binding and neutralizing antibody responses in BALB/c mice against the B.1.351 virus and other SARS-CoV-2 variants after two doses within 28 days. Immunized K18-hACE2 mice also exhibited elevated levels of live virus-neutralizing antibodies against various SARS-CoV-2 viruses. Following infection with these viruses, K18-hACE2 mice displayed a stable body weight, a high survival …

A/B Testing Of User Enrollment Forms To Enhance Diversity In The Biomedical Workforce Via The National Research Mentoring Network: User-Centered Design Case Study, Toufeeq Ahmed Syed, Erika L Thompson, Jason Johnson, Zainab Latif, Nan Kennedy, Damaris Javier, Katie Stinson, Jamboor K Vishwanatha

Faculty, Staff and Student Publications

BACKGROUND: The National Research Mentoring Network (NRMN) is a National Institutes of Health-funded program for diversifying the science, technology, engineering, math, and medicine research workforce through the provision of mentoring, networking, and professional development resources. The NRMN provides mentoring resources to members through its online platform-MyNRMN.

OBJECTIVE: MyNRMN helps members build a network of mentors. Our goal was to expand enrollment and mentoring connections, especially among those who have been historically underrepresented in biomedical training and the biomedical workforce.

METHODS: To improve the ease of enrollment, we implemented the split testing of iterations of our user interface for platform registration. …

Influence Of Polypyrrole On Phosphorus- And Tio2-Based Anode Nanomaterials For Li-Ion Batteries, Chiwon Kang, Kibum Song, Seungho Ha, Yujin Sung, Yejin Kim, Keun-Young Shin, Byung Hyo Kim

Faculty, Staff and Student Publications

Phosphorus (P) and TiO2 have been extensively studied as anode materials for lithium-ion batteries (LIBs) due to their high specific capacities. However, P is limited by low electrical conductivity and significant volume changes during charge and discharge cycles, while TiO2 is hindered by low electrical conductivity and slow Li-ion diffusion. To address these issues, we synthesized organic–inorganic hybrid anode materials of P–polypyrrole (PPy) and TiO2–PPy, through in situ polymerization of pyrrole monomer in the presence of the nanoscale inorganic materials. These hybrid anode materials showed higher cycling stability and capacity compared to pure P and TiO2. The enhancements are attributed …

Development Of The Roadway Pothole Management Program, Dingxin Cheng

Mineta Transportation Institute

Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, …

Enacting Data Context: Fixing Meaning In Transparency Data Initiatives, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

This article documents the “context cultures” underpinning efforts to develop regulations for collecting and reporting data in a United States public database known as Open Payments. Open Payments is a dataset published annually by the US Center for Medicare and Medicaid Services that documents the transfers of value from pharmaceutical and medical device manufacturers to physicians, prescribing non-physicians, and teaching hospitals. In the article, I show context became a manifold concern as differentially-situated actors engaged in modes of public advocacy and social action around not only what data meant, but also what it meant to make data meaningful. I show …

Streaminghub - A Realtime Biosignal Processing Framework For Lab Scale Experimentation, Yasith Jayawardana

Computer Science Theses & Dissertations

In human subjects research, biosignals such as eye movements, heart rate, and brain activity, are often collected and analyzed to find patterns with tangible real-world implications. Modern advancements in technology have sparked interest towards analyzing biosignals in realtime. When developing such algorithms, one may expect to find free, open-source tools that provide easy access to live, recorded, and simulated data streams. Yet, biosignal interfaces are often vendor-specific, making cross-vendor biosignal streaming non-trivial. Likewise, reading biosignal datasets is also non-trivial, as their content may be arranged quite differently.

To combat this divide, we provide the scientific community with a realtime biosignal …

Fedgmmat: Federated Generalized Linear Mixed Model Association Tests, Wentao Li, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing …

Identification Of Immune-Associated Biomarkers Of Diabetes Nephropathy Tubulointerstitial Injury Based On Machine Learning: A Bioinformatics Multi-Chip Integrated Analysis, Lin Wang, Jiaming Su, Zhongjie Liu, Shaowei Ding, Yaotan Li, Baoluo Hou, Yuxin Hu, Zhaoxi Dong, Jingyi Tang, Hongfang Liu, Weijing Liu

Faculty, Staff and Student Publications

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.

METHODS: The …