Results 31 to 40 of about 643 (115)

Enhancing Predictive Ability of Agronomic and Quality Traits in Ethiopian Malting Barley (Hordeum vulgare L.) Using Spectral Variable Selection Methods

Plant Breeding, EarlyView.
ABSTRACT Phenomic selection (PS) offers a cost‐effective, breeder‐friendly approach for public breeding programmes with limited access to genotyping or restricted financial resources for laboratory infrastructure. Since PS relies on high‐throughput phenotyping data, which is often derived from near‐infrared spectroscopy (NIRS) of harvested seeds ...
Tigist Tadesse, Peer Wilde, Wondimu Ejerso, Dorothea Borchardt, Patrick Thorwarth, Thomas Miedaner   +5 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

The Impact of Uncertainty on Forecasting the US Economy

Journal of Forecasting, Volume 45, Issue 6, Page 2703-2734, September 2026.
ABSTRACT This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production,
Angelica Ghiselli
wiley   +1 more source

Nowcasting World Trade With Machine Learning: A Three‐Step Approach

Journal of Forecasting, Volume 45, Issue 6, Page 2954-2968, September 2026.
ABSTRACT We nowcast world trade using machine learning, distinguishing between tree‐based methods (random forest and gradient boosting) and their linear‐regression‐based counterparts (macroeconomic random forest and gradient boosting—linear). While much less used in the literature, the latter are found to outperform not only the tree‐based techniques ...
Menzie Chinn, Baptiste Meunier, Sebastian Stumpner   +2 more
wiley   +1 more source

Beta Estimation Under Infrequent Trading: A Machine Learning Approach

International Review of Finance, Volume 26, Issue 3, September 2026.
ABSTRACT When shares are traded infrequently, beta estimates are often severely biased. We find that machine learning methods significantly improve forecasts of the conventional beta proxy in this infrequently traded market. They generate superior beta forecasts, statistically and economically outperforming the traditional model used by practitioners ...
Alejandro Maldonado Mendoza, Olena Onishchenko   +1 more
wiley   +1 more source

Personnel Psychology's 40 Questions Series: Artificial Intelligence

Personnel Psychology, Volume 79, Issue 3, Page 353-369, Autumn (Fall) 2026.
ABSTRACT In this article, we present a curated set of 40 questions on Artificial Intelligence (AI) to address its rapidly evolving role in Industrial/Organizational (I/O) Psychology, Human Resources (HR), and Organizational Behavior (OB) research and practice. We solicited questions from our professional networks and organized the responses into themes:
Emily D. Campion, Scott Tonidandel
wiley   +1 more source

An Integrated Process‐Based Approach for Interpreting Precipitation Isotopes Using North‐Central Texas as a Testbed

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 16, 28 August 2026.
Abstract The isotopic composition of precipitation reflects the combined influences of moisture sources, rainout history, and convective intensity, but the relative importance of these controls varies among regions and remains poorly understood in continental settings.
Juan Camacho, Ricardo Sánchez‐Murillo
wiley   +1 more source

Diagnostic Model Development for IC/BPS and Its Subtypes Using Clinical Indicators, Urinary Biomarkers, and Single‐Cell Transcriptomic Analysis

The FASEB Journal, Volume 40, Issue 15, 15 August 2026.
Subjects were divided into HC, NHIC, and HIC groups. Clinical Characteristics, urinary ultrasound parameters, and urinary biomarkers were collected for ML screening of predictive signatures. DEGs are identified from GEO transcriptomic data; scRNA‐seq delineates cell clusters, pseudotime trajectories and intercellular communication.
Cheng Luo, Yonghui Guan, Yifala Dilimulati, Xinping Zhang, Cancan Wang, Mulati Rexiati   +5 more
wiley   +1 more source

Estimation of pulmonary function from time‐resolved dynamic chest radiography using machine learning in patients with respiratory disease

Journal of Applied Clinical Medical Physics, Volume 27, Issue 8, August 2026.
Abstract Background Pulmonary function tests (PFTs), particularly spirometry, are the reference standard for assessing airflow limitation in respiratory diseases such as chronic obstructive pulmonary disease (COPD) and interstitial pulmonary disease.
Takehiro Shiinoki, Yuki Yuasa, Tsunahiko Hirano, Maki Asami‐Noyama, Kazuto Matsunaga, Hidekazu Tanaka   +5 more
wiley   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source