AIMC Topic: Algorithms

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Predicting pathogenic protein variants.

Science (New York, N.Y.)
Machine-learning algorithm uses structure prediction to spot disease-causing mutations.

Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts.

Scientific reports
This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometr...

A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI.

Neurosurgical review
Deep learning algorithms have greatly improved our ability to estimate eloquent cortex regions from resting-state brain scans for patients about to undergo neurosurgery. The use of deep learning has the potential to fully automate functional mapping ...

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Journal of biomolecular structure & dynamics
Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined o...

Principles of artificial intelligence and its application in cardiovascular medicine.

Clinical cardiology
Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This revie...

A GPU-based computational framework that bridges neuron simulation and artificial intelligence.

Nature communications
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive comp...

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

PloS one
Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mos...

Using DeepContact with Amira graphical user interface.

STAR protocols
DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira's artificial intell...

Mean-field neural networks: Learning mappings on Wasserstein space.

Neural networks : the official journal of the International Neural Network Society
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density a...

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

Magma (New York, N.Y.)
BACKGROUND: Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have fo...