Results 151 to 160 of about 3,760 (232)

Deep representation learning for temporal inference in cancer omics: a systematic literature review. [PDF]

Brief Bioinform
Prol-Castelo G, Cirillo D, Valencia A.
europepmc   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, Volume 104, Issue 8, Page 4079-4100, August 2026.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Robust graph structure learning to improve multi-omics cancer subtype classification. [PDF]

BMC Bioinformatics
Guo M, Ye X, Sakurai T.
europepmc   +1 more source

Photovoltaic Power Generation Fault Diagnosis Model Based on Multi‐Source Data Fusion Using Neural Network Algorithms

Engineering Reports, Volume 8, Issue 8, August 2026.
This study introduces a multi‐source data fusion framework for PV fault diagnosis that integrates an adaptive CNN with a collaborative data‐feature layer architecture. The model achieves 99.0% accuracy and 98.8% F1‐score, outperforming traditional methods by over 10% and offering a promising solution for reliable PV system monitoring. ABSTRACT Research
Zheng Li, Lin Wang, Huanghuang Jin, Xiaofan Zheng, Guo Zhang, Beibei Wang   +5 more
wiley   +1 more source

Learning genetic perturbation effects with variational causal inference. [PDF]

PLoS Comput Biol
Liu E, Zhang J, Uhler C.
europepmc   +1 more source

Synergistic Engineering of Nanostructures via Anodic Aluminum Oxide Templates and Atomic Layer Deposition: Design Principles, Mechanisms, and Applications

Small Structures, Volume 7, Issue 8, August 2026.
Anodic aluminum oxide (AAO) templates combined with atomic layer deposition (ALD) constitute a synergistic platform for engineering functional nanostructures within highly ordered, high‐aspect‐ratio porous architectures. By linking precursor transport modeling, surface chemistry control, and tailored ALD strategies, this review establishes a unified ...
Hyeon Joon Choi, Neul Ha, Jisu Jung, Ryeowon Kim, S. Joon Kwon, Sunghak Park, Wooseok Yang, Hyunjung Shin   +7 more
wiley   +1 more source

Sensor-Based Fault Diagnosis and Prognosis of Neurophysiological States: A Transformer Autoencoder Approach to EEG Monitoring. [PDF]

Sensors (Basel)
Moreno Escobar JJ, Castillo Pérez MD, Aguilar Del Villar EY, Espinosa HQ.   +3 more
europepmc   +1 more source

From prediction to intervention: Paradigm shifts in causal AI for precision medicine and large‐scale cohorts

VIEW, Volume 7, Issue 4, August 2026.
Large‐scale cohorts and multimodal biomedical data have enabled powerful predictive models for clinical risk stratification, but prediction alone cannot guide effective interventions. This review introduces causal artificial intelligence as a design‐first framework that integrates target trial emulation, causal discovery, and robust effect estimation ...
Linlin Cao, Yudian Xu, Yifan Chen, Honghao Huang, Wei Xu, Jun Pu   +5 more
wiley   +1 more source

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data. [PDF]

Health Data Sci
Sun C, Song M, Cai D, Zhang B, Li H, Hong S.   +5 more
europepmc   +1 more source

CESAR: A Convolutional Echo State AutoencodeR for High‐Resolution Wind Forecasting

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo
Matthew Bonas, Paolo Giani, Paola Crippa, Stefano Castruccio   +3 more
wiley   +1 more source