Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Causal knowledge graph analysis identifies adverse drug effects. [PDF]
Toonsi S, Schofield PN, Hoehndorf R.
europepmc +1 more source
Harnessing Phase Dynamics Across Diverse Frequencies with Multifrequency Oscillatory Neural Networks
Oscillatory Neural Networks (ONNs) are an emerging computing paradigm that encodes information in the phases of coupled oscillators. Traditionally, ONNs have been investigated using homogeneous frequency oscillators. However, physical hardware implementations are inherently subject to frequency mismatches, device variability, and nonuniformities.
Nil Dinç +2 more
wiley +1 more source
Neuro-symbolic NLP: taxonomy, assessment, and directions. [PDF]
Chatzikyriakidis S, Lappin S.
europepmc +1 more source
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Regularized Joint Maximum Likelihood Estimation of Latent Space Item Response Models. [PDF]
Molenaar D, Jeon M.
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Building a health system resilience framework: national, state, regional, and local perspectives. [PDF]
Paschoalotto MAC +4 more
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source

