Results 161 to 170 of about 2,935,368 (255)

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

open access: yesAdvanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary   +1 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

open access: yesAdvanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song   +6 more
wiley   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

open access: yesAdvanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi   +3 more
wiley   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

open access: yesAdvanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang   +9 more
wiley   +1 more source

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