AIMC Topic: Neural Networks, Computer

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Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Physical chemistry chemical physics : PCCP
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of prote...

AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials.

Soft matter
Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional m...

DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network.

Journal of computational biology : a journal of computational molecular cell biology
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjac...

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity.

Dermatitis : contact, atopic, occupational, drug
Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations. Our aim is ...

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation.

Medical image analysis
The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for ...

Signatures of task learning in neural representations.

Current opinion in neurobiology
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same ...

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.

Journal of cancer research and clinical oncology
PURPOSE: Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glio...

Joint learning of feature and topology for multi-view graph convolutional network.

Neural networks : the official journal of the International Neural Network Society
Graph convolutional network has been extensively employed in semi-supervised classification tasks. Although some studies have attempted to leverage graph convolutional networks to explore multi-view data, they mostly consider the fusion of feature an...

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Neuroscience
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symptoms, neuropsychiatric symptoms (NPS) commonly occur during the course of AD. Previous researches ha...

A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals.

Sensors (Basel, Switzerland)
The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin, where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of th...