Biomedical Informatics Commons^™

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

Neurotensin Regulates Primate Ovulation Via Multiple Neurotensin Receptors, Andrew C. Pearson, Jessica S. Miller, Hannah J. Jensen, Ketan Shrestha, Thomas E. Curry Jr., Diane M. Duffy

Department of Biomedical and Translational Sciences Faculty Publications

Neurotensin (NTS), a small neuropeptide, was recently established as a key paracrine mediator of ovulation. NTS mRNA is highly expressed by granulosa cells in response to the luteinizing hormone (LH) surge, and multiple NTS receptors are expressed by cells of the ovulatory follicle. To identify the role of NTS receptors NTSR1 and SORT1 in ovulation in vivo, the dominant follicle of cynomolgus macaques (Macaca fascicularis) was injected with either vehicle control, the general NTS receptor antagonist SR142948, the NTSR1-selective antagonist SR48692, or the SORT1-selective antagonist AF38469. Human chorionic gonadotropin (hCG) was then administered to initiate ovulatory events. …

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Enhancing Nonverbal Communication Through Virtual Human Technology: Protocol For A Mixed Methods Study, Analay Perez, Michael D. Fetters, John W. Creswell, Mark Scerbo, Frederick W. Kron, Richard Gonzalez, Lawrence An, Masahito Jimbo, Predrag Klasnja, Timothy C. Guetterman

Psychology Faculty Publications

Background: Communication is a critical component of the patient-provider relationship; however, limited research exists on the role of nonverbal communication. Virtual human training is an informatics-based educational strategy that offers various benefits in communication skill training directed at providers. Recent informatics-based interventions aimed at improving communication have mainly focused on verbal communication, yet research is needed to better understand how virtual humans can improve verbal and nonverbal communication and further elucidate the patient-provider dyad.

Objective: The purpose of this study is to enhance a conceptual model that incorporates technology to examine verbal and nonverbal components of communication and develop a …