Results 51 to 60 of about 3,934 (193)

From Biological Mechanisms to Causal Inference: Quantitative Systems Pharmacology and Causal Frameworks in Drug Development

Clinical Pharmacology &Therapeutics, EarlyView.
Digital patients and in silico clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision‐making by enabling the simulation of treatment effects prior to testing in patients. Among the most promising of these opportunities is the combined use of quantitative systems pharmacology (QSP) and ...
Thomas Klabunde, Ramon Hernandez, Piet H. van der Graaf, Tommaso Andreani   +3 more
wiley   +1 more source

Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines

IEEE Open Journal of Engineering in Medicine and Biology
Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the ...
Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte   +2 more
doaj   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Identifying Drivers of Predictive Aleatoric Uncertainty

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making.
Pascal Iversen, Simon Witzke, Katharina Baum, Bernhard Y. Renard   +3 more
openaire   +2 more sources

Calibration of medium‐range temperature ensemble forecasts dependent on the time horizon: An assessment

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Numerical weather prediction output necessitates calibration, as weather model systems are unable to replicate the correct statistics of observations. Such processing is especially important when predicting extreme weather. Calibration methods function by learning from prediction inadequacies during a training phase and by exploiting this information ...
Dmitrij Japs, Oleksiy Boyko, Paolo Reggiani, Ezio Todini   +3 more
wiley   +1 more source

Explainable Machine Learning for Predicting Time‐to‐Failure of Horizontal Pressurized Storage Tanks in Fire Scenarios

Safety Science and Technology, EarlyView.
An interpretable machine learning framework with SHAP analysis achieves accurate time‐to‐failure prediction for horizontal pressurized tanks under pool fires while identifying fire intensity, filling level, and wall thickness as dominant drivers of model explanation and uncertainty explanation, providing transparent and actionable insights for ...
Yubin Mu, Mingshu Bi, Zilong Deng, Di Yu, Jingjing Yang, Jingjie Ren   +5 more
wiley   +1 more source

Uncertainty Estimation of Lake Ice Cover Maps From a Random Forest Classifier Using MODIS TOA Reflectance Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This article presents a method to improve the usability of lake ice cover (LIC) maps generated from moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere reflectance data by providing estimates of aleatoric and epistemic uncertainty. We
Nastaran Saberi, Mohammad Hossein Shaker, Claude R. Duguay, K Andrea Scott, Eyke Hullermeier   +4 more
doaj   +1 more source

The evaluation crisis in artificial intelligence‐driven computational biology: Pitfalls and solutions

Quantitative Biology, Volume 14, Issue 4, December 2026.
Abstract Artificial intelligence (AI) is rapidly transforming computational biology. However, closer scrutiny reveals that many reported results do not hold under rigorous evaluation. This paper identifies prevalent, recurring issues in the field, including narrow datasets, hidden confounders that make supposedly independent test data effectively ...
Tsung Fei Khang, Joshua W. K. Ho, Lin Hou, Jiangning Song, Limsoon Wong   +4 more
wiley   +1 more source

Clinical evaluation and regression test of a commercial deep‐learning auto‐segmentation model

Journal of Applied Clinical Medical Physics, Volume 27, Issue 10, October 2026.
Abstract Background Commercial deep‐learning segmentation (DLS) tools are increasingly used in clinical practice. Software updates may alter segmentation performance, highlighting the need for systematic clinical evaluation before implementation. Purpose This study presents our experience in clinically evaluating and regression‐testing a RayStation DLS
Roya Barati, Jingwei Duan, Song Gao, Yao Zhao, Xinru Chen, Cenji Yu, Zhan Xu, Moghadaseh Khaleghibizaki, Fredrik Lofman, Jared Ohrt, Laurence Court, Peter Balter, Jinzhong Yang   +12 more
wiley   +1 more source

Generative Electronic Poetic Texts: Specifics of Verbal Component and Peculiarities of Code Structure

Научный диалог, 2017
The issues related to typification and analysis of electronic program algorithms and online services for electronic poetic texts generation are covered. Special attention is paid to the description of the algorithmic principles of generating poetic texts,
S. A. Kuchina
doaj