Results 201 to 210 of about 436,673 (317)

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Bayesian Inference of Binding Kinetics from Fluorescence Time Series. [PDF]

J Phys Chem B
Bryan JS, Tashev SA, Fazel M, Scheckenbach M, Tinnefeld P, Herten DP, Pressé S.   +6 more
europepmc   +2 more sources

Bayesian Inference of Sex-Specific Mortality Profiles and Product Yields from Unsexed Cattle Zooarchaeological Remains. [PDF]

J Archaeol Method Theory
Diekmann Y, Gillis RE, Lu Z, Rudzinski A, De Iorio M, Thomas MG.   +5 more
europepmc   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

Bayesian inference of fitness landscapes via tree-structured branching processes. [PDF]

Bioinformatics
Luo XG, Kuipers J, Rupp K, Takahashi K, Beerenwinkel N.   +4 more
europepmc   +1 more source

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Chemistry – A European Journal, EarlyView.
Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain, Max Pinheiro Jr., Matheus de Oliveira Bispo, Mario Barbatti   +3 more
wiley   +1 more source

Bayesian inference for geophysical fluid dynamics using generative models. [PDF]

Philos Trans A Math Phys Eng Sci
Lobbe A, Crisan D, Lang O.
europepmc   +1 more source

Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networks [PDF]

Minyoung Kim
openalex  

From Bayesian Inference to Neural Computation: The Analytical Emergence of Neural Network Structure from Probabilistic Relevance Estimation

Jaepil Jeong
openalex   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source