AIMC Topic: Autoencoder

Clear Filters Showing 21 to 30 of 57 articles

Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder.

Neural networks : the official journal of the International Neural Network Society Apr 29, 2025
Neural networks, while powerful, often face significant challenges in terms of interpretability, particularly in clustering tasks. Traditional methods typically rely on post-hoc explanations or supervised learning, which limit their ability to provid...
Cluster Analysis Unsupervised Machine Learning Autoencoder Neural Networks, Computer Algorithms
View on PubMed DOI

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 29, 2025
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...
Neurodevelopmental Disorders Autoencoder Attention Deficit Disorder with Hyperactivity Female Humans Machine Learning Neural Networks, Computer Child Algorithms Male Adolescent Electroencephalography Autism Spectrum Disorder
View on PubMed DOI

ConnectomeAE: Multimodal brain connectome-based dual-branch autoencoder and its application in the diagnosis of brain diseases.

Computer methods and programs in biomedicine Apr 23, 2025
BACKGROUND AND OBJECTIVE: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patient...
Alzheimer Disease Brain Diseases Connectome Deep Learning Magnetic Resonance Imaging Autoencoder Algorithms Autism Spectrum Disorder Female Male Humans Brain
View on PubMed DOI

A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports Apr 23, 2025
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
Neural Networks, Computer Autoencoder Algorithms Proteins Deep Learning Models, Molecular Computational Biology Protein Structure, Tertiary
View on PubMed DOI

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study.

Journal of medical Internet research Apr 17, 2025
BACKGROUND: Artificial patient technology could transform health care by accelerating diagnosis, treatment, and mapping clinical pathways. Deep learning methods for generating artificial data in health care include data augmentation by variational au...
Autoencoder Deep Learning Humans Feasibility Studies Proof of Concept Study
View on PubMed DOI

Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data.

The Science of the total environment Apr 16, 2025
As public awareness of environmental and health issues grows, providing accurate and accessible environmental risk information is essential for informed decision-making. Environmental indices simplify the complex impacts of various environmental fact...
Risk Assessment Deep Learning Environmental Monitoring Republic of Korea Environmental Exposure Autoencoder Air Pollutants Air Pollution
View on PubMed DOI

Automated spectral decomposition and reconstruction of optical properties using a mixed autoencoder approach.

Journal of biomedical optics Apr 9, 2025
SIGNIFICANCE: Investigating optical properties (OPs) is crucial in the field of biophotonics, as it has a broad impact on understanding light-tissue interactions. However, current techniques, such as inverse Monte Carlo simulations (IMCS), have limit...
Algorithms Optical Imaging Autoencoder Image Processing, Computer-Assisted Neural Networks, Computer Phospholipids Monte Carlo Method Scattering, Radiation
View on PubMed DOI

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics Apr 7, 2025
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...
Computational Biology Sequence Analysis, RNA Autoencoder Cluster Analysis Humans Single-Cell Analysis Software RNA-Seq Single-Cell Gene Expression Analysis Algorithms Deep Learning
View on PubMed DOI

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Sensors (Basel, Switzerland) Apr 5, 2025
Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedde...
Humans Signal Processing, Computer-Assisted Computer Security Electrocardiography Autoencoder
View on PubMed DOI

RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

IEEE transactions on neural networks and learning systems Apr 4, 2025
RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of...
Computational Biology RNA-Binding Proteins Proteins Neural Networks, Computer Algorithms Autoencoder RNA Humans
View on PubMed DOI
Popular Topics
Recent Journals