Results 51 to 60 of about 4,611 (130)

Lecture notes on ridge regression

, 2020
The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a penalty (i.e ...
van Wieringen, Wessel N.
core  

Wheat's war against stripe rust: Integrating host immunity, genomics and breeding for durable resistance

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract Wheat (Triticum aestivum L.), a foundation of global food security, faces persistent threats from stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). The pathogen thrives in cool and humid environments and regularly causes epidemics that lead to severe yield losses.
Farkhandah Jan, Mukesh Rathore, Dinesh Kumar Saini, Ayushi Singh, Naeela Qureshi, Nikita Aggarwal, Mahpara Bashir, Mehnaz Shakeel, Vikas Gupta, Sundeep Kumar, Rajeev Kumar Varshney, Reyazul Rouf Mir   +11 more
wiley   +1 more source

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome‐wide polymorphisms

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract The growing size of genome‐wide polymorphism data in animal and plant breeding has raised concerns regarding computational load and time, particularly when predicting genetic values for target traits using genomic prediction. Several deep learning and conventional methods, including dimensionality reduction techniques such as principal ...
Tanzila Raihan, Chyon Hae Kim, Hiroyuki Shimono, Akio Kimura, Hiroyoshi Iwata   +4 more
wiley   +1 more source

A benchmarking of genomic selection models for predicting grain‐yield related traits using haplotype‐based and genome‐wide association study‐based markers in rice

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract Rice (Oryza sativa) is an important staple food, feeding more than half of the global population. A feasible improvement of rice yield is necessary to meet the ever–growing food demands. Genomic selection (GS), as an advanced breeding technique, enables the prediction of phenotypes solely based on genotypic data using a constructed genomic ...
Xiankang Hu, Liu Xian, Guangping Cao, Shuoran Liu, Di Wang, Yanru Che, Weichao Huang, Yanhui Lin, Liqiang He   +8 more
wiley   +1 more source

(Non) Linear Regression Modeling [PDF]

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
core  

Comprehensive Hydrogen Evolution Descriptors for Nonprecious Metal Oxide Electrocatalysts

ChemElectroChem, Volume 13, Issue 9, 5 May 2026.
25 kinds of Fe‐, Co‐, and Ni‐based oxides are investigated to unveil the comprehensive descriptor for electrocatalytic hydrogen evolution reaction (HER) in metal oxides. Their HER activities are affected by the number of d electrons of the catalysts; i.e., more and less d electrons are beneficial for Fe‐ and Ni‐based oxides, respectively.
Yuuki Sugawara, Issei Kohara, Yuki Saito, Yosuke Hamasaki, Keigo Kamata, Mitsuru Itoh, Takeo Yamaguchi   +6 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, Volume 36, Issue 38, 11 May 2026.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Digital Technologies Disclosure and the Cost of Capital: The Mediating Role of Sustainability Performance

Business Strategy and the Environment, Volume 35, Issue 4, Page 5895-5925, May 2026.
ABSTRACT This study examines the economic consequences of Digital Technologies Disclosure (DTD), focusing on its impact on the cost of capital. The increasing significance of digital transformation in shaping corporate strategies and market perceptions motivates the study.
Hussein Mohsen Saber Ahmed, Awad Elsayed Awad Ibrahim, Khaldoon Albitar   +2 more
wiley   +1 more source

Performance Benchmarking of Optimized Stacked Ensemble Frameworks in Blood Glucose Level (BGL) Prediction

Engineering Reports, Volume 8, Issue 5, May 2026.
Optimized stacked ensemble framework for precise Blood Glucose Level (BGL) prediction, demonstrating improved accuracy and reliability through integrated machine learning models. ABSTRACT This paper presents a study of different stacked ensemble techniques for long‐term Blood Glucose Level (BGL) prediction in adults.
Ashwani Kharola, Harvinder Singh, Jeewan Singh, Purnendu Shekar Pandey, Nitin Kumar, Ajay Kumar, Abhijit Bhowmik, Vivek John, Yohannes Mengist   +8 more
wiley   +1 more source

A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention

Obesity Reviews, Volume 27, Issue 5, May 2026.
ABSTRACT This systematic review examines the applications of artificial intelligence (AI) in preventing obesity, addressing a critical public health issue that affects a substantial portion of the population. With obesity rates rising alarmingly, particularly in the United States, this review synthesizes findings from 46 studies published between 2008 ...
Atefehsadat Haghighathoseini, Shuo‐yu Lin, Ge Song, Ruopeng An, Hong Xue   +4 more
wiley   +1 more source