Eight quick tips for requesting materials from natural history collections. [PDF]
Scorsim Omura GY +2 more
europepmc +1 more source
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
Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction. [PDF]
Rayka M, Naghavi SS.
europepmc +1 more source
Majority‐Voting Overlapping Method for Error Correction in DNA Data Storage
We propose an overlapping‐based majority‐voting method for DNA data storage error correction. By aligning multiple reads and choosing the most frequent base per position, it suppresses substitution errors without prior models. Validated on synthetic and real sequencing data, it achieves high‐fidelity, scalable, and cost‐effective reconstruction ...
Thi Bich Ngoc Nguyen +5 more
wiley +1 more source
Agent-based modeling identifies multiple paths for inflammation resolution. [PDF]
Desgrez-Dautet H +4 more
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Structured approaches to improving outcomes of oral exam in medical and paramedical education: A systematic review and evidence-based framework. [PDF]
Torab-Miandoab A +3 more
europepmc +1 more source
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha +2 more
wiley +1 more source
A Technical Framework for Predicting Regulatory Deficiencies in Pharmaceutical Chemistry, Manufacturing, and Controls (CMC) Submissions Using Machine Learning. [PDF]
Modi P.
europepmc +1 more source
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source

