AIMC Topic: Neural Networks, Computer

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scMUSCL: multi-source transfer learning for clustering scRNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis relies heavily on effective clustering to facilitate numerous downstream applications. Although several machine learning methods have been developed to enhance single-cell clustering, most a...

Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning.

Scientific reports
Hyperspectral imaging (HSI) has shown significant diagnostic potential for both intra- and postoperative perfusion assessment. The purpose of this study was to combine machine learning and neural networks with HSI to develop a method for detecting fl...

Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.

European radiology experimental
BACKGROUND: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neura...

The Integration of Artificial Intelligence and Augmented Reality in Dental Education: Current Applications and Future Potential.

Compendium of continuing education in dentistry (Jamesburg, N.J. : 1995)
The integration of artificial intelligence (AI) and augmented reality (AR) in dental education is transforming how students acquire and refine clinical skills. This article explores current applications and the future potential of AI and AR in dental...

RLEAAI: improving antibody-antigen interaction prediction using protein language model and sequence order information.

Briefings in bioinformatics
Antibody-antigen interactions (AAIs) are a pervasive phenomenon in the natural and are instrumental in the design of antibody-based drugs. Despite the emergence of various deep learning-based methods aimed at enhancing the accuracy of AAIs prediction...

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.

Briefings in bioinformatics
Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines ...

GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.

Briefings in bioinformatics
Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on gra...

Short-Dipole Sensor Response Linearization Through Physics-Informed Neural Networks.

Bioelectromagnetics
Short-dipole diode sensors loaded with highly resistive lines are commonly used to measure the time-averaged square of the high-frequency electromagnetic field amplitude directly. Their precision, simplicity, broadband, high dynamic range capability,...

Leukaemia Stem Cells and Their Normal Stem Cell Counterparts Are Morphologically Distinguishable by Artificial Intelligence.

Journal of cellular and molecular medicine
Leukaemia stem cells (LSCs) are a rare population among the bulk of leukaemia cells and are responsible for disease initiation, progression/relapse and insensitivity to therapies in numerous haematologic malignancies. Identification of LSCs and monit...

[Evaluation of the possibility of using neural networks for automatic diagnostics of obstructive urination].

Urologiia (Moscow, Russia : 1999)
INTRODUCTION: Obstructive type of urination requires accurate and timely diagnosis to prevent complications and improve the quality of life of patients. Traditional diagnostic methods such as uroflowmetry, although they remain the standard, have thei...