Results 181 to 190 of about 399,768 (265)

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks. [PDF]

J Med Internet Res
Etholén A, Roos T, Hänninen M, Bouri I, Kulmala J, Rahkonen O, Kouvonen A, Lallukka T.   +7 more
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Natural Gas Purification Plants Based on Interpretive Structural Models and Bayesian Networks. [PDF]

ACS Omega
Gong J, Li J, Tan L, Zhang L, Wang Y, Zhao J.   +5 more
europepmc   +1 more source

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

Advanced 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, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Assessing Credibility in Bayesian Networks Structure Learning. [PDF]

Entropy (Basel)
Barth V, Serrão F, Maciel C.
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

Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study. [PDF]

Front Pain Res (Lausanne)
Al-Khinji AAMA, Malouche D.
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer. [PDF]

JCO Clin Cancer Inform
Gonzalez BD, Li X, Gudenkauf LM, Pullukkara JJ, Oswald LB, Hoogland AI, Le T, El Naqa I, Saltos AN, Haura EB, Luo Y.   +10 more
europepmc   +1 more source