Results 191 to 200 of about 85,546 (299)

Bayesian Joint Modeling for Longitudinal Magnitude Data With Informative Dropout: An Application to Critical Care Data. [PDF]

Biom J
Teng W, Ferguson ND, Goligher EC, Heath A.   +3 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, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Predicting Dropout in Psychotherapy for Major Depressive Disorder: A Machine Learning Approach to Identifying At-Risk Patients. [PDF]

Clin Psychol Psychother
Bremer-Hoeve S, van Bronswijk SC, Beekman ATF, Miggiels M, Bruijniks SJE, van Dijk MK.   +5 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
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, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Mentes ativas (active minds): effects of a remote cognitive stimulation program. [PDF]

Dement Neuropsychol
da Silva SA, Dos Santos G, Ordonez TN, de Souza MAA, Bacelar DDS, Rodrigues YDS, Fioramonte BM, Gutierrez BAO, da Silva TBL.   +8 more
europepmc   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

Defining and reporting treatment dropout in blended therapy for mental health: scoping review and analysis. [PDF]

NPJ Digit Med
Eicher SC, Fenski F, Behr S, Hammelrath L, Boettcher J, Schaeuffele C, Knaevelsrud C.   +6 more
europepmc   +1 more source

Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network

Advanced Intelligent Discovery, EarlyView.
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang, Bing Sun, Zhigang Chen, Chenyu Zhao, Eugene Bakker, Yaojie Lei, Lingfei Zhao, Shijian Wang, Chao Liu, Virginia Xiaojun He, Bahram Shirzadi, Hao Liu, Xin Xi, Ruiliu Liu, Dongqing Wu, Guodong Long, Guoxiu Wang   +16 more
wiley   +1 more source

Experiences From an Internet-Delivered Treatment Program for Individuals With Obesity: Pilot Study. [PDF]

JMIR Form Res
Anderzen-Carlsson A, Imhagen A, Jansson S, Galavazi M, Karlsson J.   +4 more
europepmc   +1 more source

Physics‐Guided Modeling of Temperature‐Dependent Electrical Conductivity of Functional Oxides

Advanced Intelligent Discovery, EarlyView.
This complete workflow presents an automated, data‐driven screening of functional oxides based on their predicted electrical conductivity. A curated dataset is fed into TriCondNet, a charge‐transport, physics‐guided neural network that classifies an input composition's conduction mechanism and then simulates its conductivity–temperature profile.
Elias Assaad, Jae Evans, Troy D. Manning, Matthew J. Rosseinsky, Kerys Trathen, Bei Peng, Matthew S. Dyer   +6 more
wiley   +1 more source