AIMC Topic: 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...
Multiomics Autoencoder Single-Cell Analysis Computational Biology Software Algorithms 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...
Humans Algorithms Sequence Analysis, RNA Autoencoder RNA-Seq Computational Biology Deep Learning Single-Cell Analysis
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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 Transcriptome Mice Computational Biology Hippocampus Neural Networks, Computer Animals Software Gene Expression Profiling Algorithms Humans Brain
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Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.

Radiology. Artificial intelligence Mar 1, 2025
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospecti...
Neural Networks, Computer Algorithms Humans Middle Aged Deep Learning Magnetic Resonance Imaging Retrospective Studies Male Autoencoder Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Prostatic Neoplasms Prostate
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SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.

Bioinformatics (Oxford, England) Feb 4, 2025
MOTIVATION: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).
Computational Biology Supervised Machine Learning Humans Drug Discovery Disease Autoencoder
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Autoencoder techniques for survival analysis on renal cell carcinoma.

PloS one Jan 1, 2025
Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the mos...
Survival Analysis Kidney Neoplasms Antibodies, Monoclonal, Humanized Progression-Free Survival Autoencoder Humans Proportional Hazards Models Transcriptome Axitinib Immunotherapy Carcinoma, Renal Cell Sunitinib
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Unsupervised Anomaly Detection by Learning Elastic Transformations Within an Autoencoder Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Machine learning has approached magnetic resonance image (MRI) analysis using multiple techniques, including deep supervised learning methodologies, where lesions such as tumors or features associated with defined pathologies have been identified sat...
Image Processing, Computer-Assisted Algorithms Unsupervised Machine Learning Humans Magnetic Resonance Imaging Brain Autoencoder Machine Learning
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