Results 201 to 210 of about 215,422 (309)

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

Adherence to ecological momentary assessment studies in children and adolescents with psychopathology: A systematic review with meta-analysis. [PDF]

NPP Digit Psychiatry Neurosci
Henry LM, Pandya U, Malone E, Miller C, Agyemang S, Tandilashvili E, Lewis K, Haeffner CE, Lee K, Livinski AA, Kiderman M, Naim R, Metcalf O, Lopez-Guzman S, Brotman MA.   +14 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

Spacing effect improves generalization in biological and artificial systems. [PDF]

Patterns (N Y)
Sun G, Huang N, Yan H, Zhou J, Li Q, Lei B, Zhong Y, Wang L.   +7 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

Detect and Repair: Robust Self-Supervised Wearable Sensing Under Missing Modalities. [PDF]

Sensors (Basel)
Cisse AH, Ishimaru S.
europepmc   +1 more source

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article describes a multimodal fusion data acquisition and processing system about electromyography for dynamic movement recognition and bioelectrical impedance for key posture recognition. In addition, a new dynamic–static fusion algorithm strategy is designed.
Chenhao Cao, Gang Ma, Zelin Chen, Yiming Ouyang, Hu Jin, Shiwu Zhang   +5 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

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

Autonomous Uncertainty Quantification for Computational Point-of-Care Sensors. [PDF]

ACS Nano
Goncharov A, Ghosh R, Joung HA, Di Carlo D, Ozcan A.   +4 more
europepmc   +1 more source