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BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.
PLoS Comput Biol
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance).
View Article and Find Full Text PDFConvolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation.
PLoS One
January 2025
Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
CNN is considered an efficient tool in brain image segmentation. However, neonatal brain images require specific methods due to their nature and structural differences from adult brain images. Hence, it is necessary to determine the optimal structure and parameters for these models to achieve the desired results.
View Article and Find Full Text PDFValidating a novel measure for assessing patient openness and concerns about using artificial intelligence in healthcare.
Learn Health Syst
January 2025
Bioethics Research Center, Division of General Medical Sciences, Department of Medicine Washington University School of Medicine St. Louis Missouri USA.
Objectives: Patient engagement is critical for the effective development and use of artificial intelligence (AI)-enabled tools in learning health systems (LHSs). We adapted a previously validated measure from pediatrics to assess adults' openness and concerns about the use of AI in their healthcare.
Study Design: Cross-sectional survey.
Implementing a Responsible Conduct of Research Curriculum for an Undergraduate Summer Clinical Research Program in Quantitative Sciences.
J Med Educ Curric Dev
January 2025
Department of Health Policy and Management, Columbia University Mailman School of Public Health, New York, NY, USA.
Objectives: Instilling the principles of ethical and responsible medical research is critical for educating the next generation of clinical researchers. We developed a responsible conduct of research (RCR) workshop and associated curriculum for undergraduate trainees in a quantitative clinical research program.
Methods: Topics in this 7-module RCR workshop are relevant to undergraduate trainees in quantitative fields, many of whom are learning about these concepts for the first time.
Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems.
Water Res X
December 2024
Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.
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