Results 41 to 50 of about 4,611 (130)

Decoding temporal miRNA signatures of semen under in vitro exposure for forensic time since deposition estimation using machine learning‐driven modeling

Interdisciplinary Medicine, EarlyView.
This study develops a novel miRNA‐based framework for estimating the time since deposition of semen stains, combining small RNA sequencing with machine learning. Time‐dependent miRNA modules were identified using Mfuzz clustering and WGCNA, followed by a multi‐stage feature selection pipeline that reduced 261 candidate miRNAs to a minimal 7‐miRNA panel.
Meiming Cai, Qiong Lan, Tong Xie, Qinglin Liu, Ming Zhao, Xiaolian Wu, Xin Shi, Ruonan Shen, Yiman Wu, Chen Mao, Bin Cong, Bofeng Zhu   +11 more
wiley   +1 more source

The needle study: Machine learning as a new method for case‐finding in celiac disease

Journal of Pediatric Gastroenterology and Nutrition, EarlyView.
Abstract Objectives Despite a well‐defined diagnostic work‐up, uncertainties persist regarding celiac disease (CeD) detection strategies in the general population. Machine learning (ML) algorithms offer promise in aiding medical decision‐making on clinical data.
Chiara Maria Trovato, Monica Montuori, Maria Ludovica Costanzo, Giusy Russo, Cosimo Ruggiero, Danila Volpe, Francesca Ferretti, Antonella Diamanti, Carlo Ciliberto, Salvatore Oliva   +9 more
wiley   +1 more source

Opening the Black Box of Nonprofit Reputation and Volunteer Attraction With Supervised Machine Learning

Nonprofit Management and Leadership, EarlyView.
ABSTRACT With the aim to explore the potential of machine learning for nonprofit research, this article contrasts traditional linear regression with four contemporary supervised machine learning approaches. Concretely, we predict (1) reputation ratings and (2) the total number of volunteers for 4021 non‐profit organizations in the U.S.
Moritz Schmid, Hannes Lampe, Jurgen Willems   +2 more
wiley   +1 more source

Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations

Quality and Reliability Engineering International, EarlyView.
ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim, Huadong Mo, Hemanshu Pota, Daoyi Dong   +3 more
wiley   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Sparse Warcasting

Scottish Journal of Political Economy, EarlyView.
ABSTRACT Forecasting economic activity during institutional collapse requires nowcasts derived exclusively from alternative data sources. Such sources are abundant yet theoretically unanchored and potentially weakly informative. This study examines whether sparse supervised dimension reduction extracts reliable signals in a context rich in data but ...
Mihnea Constantinescu
wiley   +1 more source

Hierarchical shrinkage priors for dynamic regressions with many predictors [PDF]

This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarchical Normal-Gamma priors. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using this ...
Korobilis, Dimitris
core   +1 more source

Early prediction of transfusion requirements in trauma patients using explainable machine learning

Transfusion, EarlyView.
Abstract Introduction Hemorrhagic shock is the most common preventable cause of death in trauma patients. Early transfusion significantly improves survivability in patients suffering from hemorrhagic shock. We hypothesized that machine learning models could reduce time to initial transfusion by more rapidly identifying patients likely to require blood ...
Michael R. De La Rosa, Alexander Bowers, Brian Eastridge, Min Wang, Donald Jenkins   +4 more
wiley   +1 more source

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
Raysa Gevartosky, Lucas Berger Munaro, Jessica E. Rutkoski   +2 more
wiley   +1 more source

Aspartame Increases the Risk of Pancreatic Ductal Adenocarcinoma

eFood, Volume 7, Issue 3, June 2026.
Aspartame (APM) is a widely used artificial sweetener associated with various health concerns, including potential links to diabetes, cardiovascular diseases, and an increased risk of cancer. A comprehensive approach incorporating data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample ...
Jumin Xie, Wei Fang, Zean Song, Zhang Yu, Ying Zhu, Shaoping Liu   +5 more
wiley   +1 more source