AIMC Topic: Neural Networks, Computer

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Leveraging infrared spectroscopy for cocoa content prediction: A dual approach with Kohonen neural network and multivariate modeling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
People of all ages enjoy chocolate, and its popularity is attributed to its pleasant taste and aroma, as well as its associated health benefits. Produced through both artisanal and industrial processes, which involve harvesting, selecting, fermenting...

VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.

Briefings in bioinformatics
The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current ...

spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration.

Briefings in bioinformatics
Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to th...

Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Neurosurgical focus
OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine fro...

Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.

Journal of vision
Visual experience is known to play a critical role in face recognition. This experience is thought to enable the formation of a view-invariant representation by learning which features are critical to identify faces across views. Discovering these cr...

Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.

Journal of the experimental analysis of behavior
Traditionally, the experimental analysis of behavior has relied on the single discrete response paradigm (e.g., key pecks, lever presses, screen clicks) to identify behavioral patterns. However, the development and availability of new technology allo...

Spiking Neural Network Models of Interaural Time Difference Extraction via a Massively Collaborative Process.

eNeuro
Neuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. At this scale, such projects can tackle big challenges and engage diverse participants. Inspired by projects in mathematics, ...

Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks.

Analytical chemistry
Early diagnosis of kidney disease is crucial for treatment and prognosis. Compared with kidney biopsy, a noninvasive urine-based diagnosis method of kidney disease can be more convenient and less painful for patients. Urine is closely associated with...

A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.

Medical engineering & physics
Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats...

Differentiable graph clustering with structural grouping for single-cell RNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks...