AIMC Topic: Autoencoder

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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 Deep Learning Computational Biology Algorithms Protein Structure, Tertiary Proteins Autoencoder Models, Molecular
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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...
Humans Autoencoder Deep Learning Proof of Concept Study Feasibility Studies
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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...
Republic of Korea Risk Assessment Deep Learning Air Pollution Environmental Exposure Autoencoder Environmental Monitoring Air Pollutants
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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...
Autoencoder Optical Imaging Scattering, Radiation Neural Networks, Computer Algorithms Phospholipids Monte Carlo Method Image Processing, Computer-Assisted
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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...
Humans Software Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Algorithms Deep Learning Cluster Analysis Autoencoder Computational Biology Sequence Analysis, RNA
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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...
Computer Security Autoencoder Electrocardiography Signal Processing, Computer-Assisted Humans
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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...
RNA Autoencoder Neural Networks, Computer RNA-Binding Proteins Algorithms Computational Biology Humans Proteins
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scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.

Genome biology Mar 21, 2025
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensio...
Deep Learning Gene Expression Profiling Humans Software Transcriptome Single-Cell Analysis Autoencoder
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Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks.

Scientific reports Mar 13, 2025
Parkinson's disease is a neurodegenerative disorder that is associated with aging, leading to the progressive deterioration of certain regions of the brain. Accurate and timely diagnosis plays a crucial role in facilitating optimal therapy and improv...
Autoencoder Deep Learning Parkinson Disease Male Middle Aged Neural Networks, Computer Humans Female Aged
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NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Mar 12, 2025
Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for disease detection. However, current AI systems require extensive expert-labeled data for each specific medical condition, limiting their applications. In th...
Endoscopy Humans Image Interpretation, Computer-Assisted Nose Diseases Artificial Intelligence Autoencoder Machine Learning Algorithms
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