Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Interannual Variation in Seed Traits of <i>Cedrela</i> Species: Implications for Conservation in the Context of Climate Change. [PDF]
Plants (Basel)Galíndez G +9 more
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
A New Species of <i>Pycnospatha</i> (Araceae) from Eastern Thailand, with an Updated Key to All Known Species. [PDF]
Life (Basel)Promprom W +4 more
europepmc +1 more source
Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics
Advanced Intelligent Discovery, EarlyView.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
Habitat, seasonal temperature and collection year drive variable germination responses in the endangered plant <i>Harperocallis flava</i>. [PDF]
Conserv PhysiolGardner AG, Pérez HE.
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration. [PDF]
Sci RepAli OM +3 more
europepmc +1 more source
Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance
Advanced Intelligent Discovery, EarlyView.Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen +4 more
wiley +1 more source