Results 61 to 70 of about 294 (197)

BiGraph‐DTA: Predicting drug–target interactions of hepatoprotective agents with graph convolutional networks

Quantitative Biology, Volume 14, Issue 1, March 2026.
Abstract Predicting drug–target affinity (DTA) is critical for discovering and developing hepatoprotective agents that can prevent and treat liver diseases. In this study, we propose BiGraph‐DTA, a new predictive model for identifying DTA score prediction for hepatoprotective compounds by combining graph convolutional networks and bidirectional long ...
Arief Sartono, Bambang Riyanto Trilaksono, Sophi Damayanti, Anto Satriyo Nugroho, Firdayani Firdayani   +4 more
wiley   +1 more source

Longitudinal Alzheimer’s Disease Progression Modelling via Hybrid Vision Transformers and Recurrent Neural Networks With Cross‐Modal Feature Fusion

Expert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Modelling the evolution of Alzheimer's disease (AD) requires a thorough spatiotemporal study of longitudinal neuroimaging data. We propose in this paper a novel deep learning framework that uses a parallel combination of Recurrent Neural Networks (RNNs) and Vision Transformers (ViT) to extract temporal disease dynamics and spatial structural ...
Sahbi Bahroun, Gwanggil Jeon
wiley   +1 more source

Unlocking IIoT Potential: A Systematic Review of AI Applications, Adoption Drivers, and Implementation Barriers

Applied AI Letters, Volume 7, Issue 1, February 2026.
Unlocking IIoT Potential with AI. ABSTRACT Artificial Intelligence (AI) is playing an increasingly vital role in the Industrial Internet of Things (IIoT), enabling predictive analytics, real‐time monitoring, and autonomous operations across industries such as manufacturing, logistics, and energy.
Tinashe Magara, Mampilo Phahlane
wiley   +1 more source

Toward precision oncology: An integrative multi‐omics approach for prognosis prediction and inferred immunotherapy responsiveness in breast cancer

Clinical and Translational Discovery, Volume 6, Issue 1, February 2026.
We integrated four omics to build a breast cancer immunotherapy predictor. Survival‐associated biomarkers were compressed into 200 latent features via an autoencoder, then refined using three survival models. K‐means defined two subgroups (C1, high‐risk and C2, low‐risk).
Houda Bendani, Nasma Boumajdi, Lahcen Belyamani, Azeddine Ibrahimi   +3 more
wiley   +1 more source

Radiogenomics: Current Understandings and Future Perspectives

MedComm, Volume 7, Issue 2, February 2026.
Radiogenomics links imaging phenotypes with genetic variations, offering potential for comprehensive understanding, cost‐effective diagnosis, and prognosis prediction to advance personalized medicine. However, its clinical application remains limited by several challenges.
Xinyu Zhang, Qingpei Lai, Jin Cao, Jerry Chi Fung Ching, Xinzhi Teng, Jiang Zhang, Shara Wee Yee Lee, Ge Ren, Jing Cai   +8 more
wiley   +1 more source

Optimized reconstruction of undersampled Dixon sequences using new memory‐efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN

Magnetic Resonance in Medicine, Volume 95, Issue 2, Page 1189-1204, February 2026.
Abstract Purpose Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and assessing disease severity in neuromuscular diseases. Undersampling of MRI acquisitions is commonly used to reduce scanning time. The present paper introduces novel unrolled neural networks for the
Sandra Martin, Amira Trabelsi, Maxime Guye, Marc Dubois, Redha Abdeddaim, David Bendahan, Rémi André   +6 more
wiley   +1 more source

Forecasting Local Ionospheric Parameters Using Transformers

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago, C. W. Curtis, A. T. Ihler, D. P. Drob, K. A. Zawdie   +4 more
wiley   +1 more source

Optimising Image Feature Extraction and Selection: A Comprehensive Review With Spark Case Studies

Expert Systems, Volume 43, Issue 2, February 2026.
ABSTRACT As benchmark image datasets expand in sample size and feature complexity, the challenge of managing increased dimensionality becomes apparent. Contrary to the expectation that more features equate to enhanced information and improved outcomes, the curse of dimensionality often hampers performance.
J. Guzmán Figueira‐Domínguez, Beatriz Remeseiro, Verónica Bolón‐Canedo   +2 more
wiley   +1 more source

Remaining Useful Life Prediction in an Aerospace Engine: A Multivariable Fuzzy Time Series Classification Approach

Expert Systems, Volume 43, Issue 2, February 2026.
ABSTRACT Failures in safety‐critical systems such as aircraft engines pose severe economic and societal risks. This study introduces a novel Remaining Useful Life (RUL) prediction method uniquely combining diverse techniques. Specifically, the proposed methodology integrates fuzzy time series analysis with sliding window segmentation and Multinomial ...
Luiz Rogério de Freitas Júnior, Frederico Gadelha Guimarães   +1 more
wiley   +1 more source

Performance of serial concatenated convolutional codes with MSK over ISI wireless channels [PDF]

, 2003
Le Feng
openalex