AIMC Topic: Computer Simulation
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In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.
Journal of computer-aided molecular design
Dec 31, 2024
Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (...
Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.
Environmental and molecular mutagenesis
Dec 30, 2024
Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode o...
Fault diagnosis of nonlinear analog circuits using generalized frequency response function and LSSVM.
PloS one
Dec 30, 2024
A fault diagnosis method of nonlinear analog circuits is proposed that combines the generalized frequency response function (GFRF) and the simplified least squares support vector machine (LSSVM). In this study, the harmonic signal is used as an input...
Chaotic recurrent neural networks for brain modelling: A review.
Neural networks : the official journal of the International Neural Network Society
Dec 27, 2024
Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous ac...
Distributed aggregative optimization with affine coupling constraints.
Neural networks : the official journal of the International Neural Network Society
Dec 26, 2024
This paper investigates a distributed aggregative optimization problem subject to coupling affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents'...
Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.
Neural networks : the official journal of the International Neural Network Society
Dec 26, 2024
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy...
Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion terms.
Neural networks : the official journal of the International Neural Network Society
Dec 24, 2024
This paper investigates the probabilistic-sampling-based asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs). Aiming at mitigating the drawback of the well-known fixed-sampling control law, a more general probabi...
Computer model for gait assessments in Parkinson's patients using a fuzzy inference model and inertial sensors.
Artificial intelligence in medicine
Dec 24, 2024
Patients with Parkinson's disease (PD) in the moderate and severe stages can present several walk alterations. They can show slow movements and difficulty initiating, varying, or interrupting their gait; freezing; short steps; speed changes; shufflin...
Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.
EBioMedicine
Dec 24, 2024
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study ai...
Performance of recurrent neural networks with Monte Carlo dropout for predicting pharmacokinetic parameters from dynamic contrast-enhanced magnetic resonance imaging data.
Journal of applied clinical medical physics
Dec 23, 2024
PURPOSE: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynam...
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