AIMC Topic: Autoencoder

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Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis.

Computers in biology and medicine Jan 1, 2025
Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning me...
Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Neural Networks, Computer Heart Algorithms Deep Learning Autoencoder
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Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings.

Neural networks : the official journal of the International Neural Network Society Dec 4, 2024
This article proposes a novel deep clustering model based on the variational autoencoder (VAE), named GamMM-VAE, which can learn latent representations of training data for clustering in an unsupervised manner. Most existing VAE-based deep clustering...
Bayes Theorem Normal Distribution Autoencoder Humans Algorithms Neural Networks, Computer Deep Learning Cluster Analysis
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Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.

Scientific reports Jul 1, 2025
Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision medicine approaches have had limited success, emphasizing the need for deeper insights into cellular su...
Algorithms Gene Expression Profiling Transcriptome Humans Keratinocytes Gingiva Autoencoder Single-Cell Analysis Sequence Analysis, RNA Principal Component Analysis
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Speech signals-based Parkinson's disease diagnosis using hybrid autoencoder-LSTM models.

Computers in biology and medicine Jul 1, 2025
Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages ha...
Male Middle Aged Speech Parkinson Disease Signal Processing, Computer-Assisted Autoencoder Neural Networks, Computer Female Aged Humans
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GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

Computers in biology and medicine Jun 1, 2025
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.
Female Aged Male Humans Biomarkers Autoencoder Single-Cell Analysis Sequence Analysis, RNA Alzheimer Disease Leukocytes, Mononuclear Deep Learning
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Medication Recommender System for ICU Patients Using Autoencoders.

Studies in health technology and informatics May 15, 2025
Patients admitted to the intensive care unit (ICU) are often treated with multiple high-risk medications. Over- and underprescribing of indicated medications, and inappropriate choice of medications frequently occur in the ICU. This risk has to be mi...
Medical Order Entry Systems Humans Intensive Care Units Electronic Health Records Decision Support Systems, Clinical Autoencoder
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GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.

Briefings in bioinformatics May 1, 2025
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...
Algorithms Gene Regulatory Networks Computational Biology Humans Neural Networks, Computer Autoencoder
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FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.

Briefings in bioinformatics Mar 4, 2025
Single-cell multi-omics technologies have revolutionized the study of cell states and functions by simultaneously profiling multiple molecular layers within individual cells. However, existing methods for integrating these data struggle to preserve c...
Autoencoder Computational Biology Multiomics Software Algorithms Single-Cell Analysis Humans Neural Networks, Computer
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CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.

Briefings in bioinformatics Mar 4, 2025
With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding t...
Deep Learning Single-Cell Analysis RNA-Seq Computational Biology Sequence Analysis, RNA Autoencoder Humans Algorithms
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MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.

Briefings in bioinformatics Mar 4, 2025
Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detectin...
Autoencoder Animals Gene Expression Profiling Transcriptome Mice Software Computational Biology Algorithms Hippocampus Humans Brain Neural Networks, Computer
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