AIMC Topic: Neural Networks, Computer

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AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...

Optimizing the early-stage of composting process emissions - artificial intelligence primary tests.

Scientific reports
Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH, CO and HS, as well as greenhouse gases such as CO. One promising approach to enhancing composting conditions...

Neural network-based automated proptosis measurement using computed tomography images for patients with thyroid-associated orbitopathy.

Scientific reports
The purpose of this study was to evaluate the clinical feasibility and reliability of a neural network (NN)-based automated proptosis measurement system using computed tomography (CT) images. An automated proptosis measurement system was developed us...

A hybrid local-global neural network for visual classification using raw EEG signals.

Scientific reports
EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features f...

Anchoring temporal convolutional networks for epileptic seizure prediction.

Journal of neural engineering
. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to ...

A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.

Nature computational science
Deep generative models are gaining attention in the field of de novo drug design. However, the rational design of ligand molecules for novel targets remains challenging, particularly in controlling the properties of the generated molecules. Here, ins...

A deep learning model for prediction of autism status using whole-exome sequencing data.

PLoS computational biology
Autism is a developmental disability. Research demonstrated that children with autism benefit from early diagnosis and early intervention. Genetic factors are considered major contributors to the development of autism. Machine learning (ML), includin...

Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiac balanced steady state free precession (bSSFP) cine imaging suffers from banding and flow artifacts induced by off-resonance. The work aimed to develop a twofold phase cycling sequence with a neural network-based reconstruction (2P...

A machine-learning-based algorithm for bone marrow cell differential counting.

International journal of medical informatics
BACKGROUND: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed.

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Molecular pharmaceutics
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive ...