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Monte Carlo Method

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A Bayesian convolutional neural network-based generalized linear model.

Biometrics
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in pred...

NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...

Empirical validation of Conformal Prediction for trustworthy skin lesions classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisions by providing complementary information, particularly within high-r...

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input.

Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability.

The Science of the total environment
Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in ...

URCA: Uncertainty-based region clipping algorithm for semi-supervised medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Training convolutional neural networks based on large amount of labeled data has made great progress in the field of image segmentation. However, in medical image segmentation tasks, annotating the data is expensive and time...

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.

Medical decision making : an international journal of the Society for Medical Decision Making
PURPOSE: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally valida...

Integrating machine learning with -SAS for enhanced structural analysis in small-angle scattering: applications in biological and artificial macromolecular complexes.

The European physical journal. E, Soft matter
Small-Angle Scattering (SAS), encompassing both X-ray (SAXS) and Neutron (SANS) techniques, is a crucial tool for structural analysis at the nanoscale, particularly in the realm of biological macromolecules. This paper explores the intricacies of SAS...

Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.

Radiation protection dosimetry
Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The mu...

Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks.

Mathematical biosciences
The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction-diffusion processes which model such systems is highly computationally expensive, the cost ...