AIMC Topic: Autoencoder

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Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.

Scientific reports
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...

Learning salient representation of crashes and near-crashes using supervised contrastive variational autoencoder.

Accident; analysis and prevention
Models capable of learning representations that are salient in safety-critical events (SCEs; including crashes and near-crashes) are crucial for road safety. This study proposes a novel deep learning model, the supervised contrastive variational auto...

Intraoperative Assessment of Parathyroidectomy Outcomes via Autoencoder-Support-Vector-Machine-Assisted Label-Free Differential SERS Spectroscopy.

Nano letters
Intraoperative guidance plays a pivotal role in enhancing surgical success rates and optimizing patients' prognosis. However, during surgery, the lack of reliable monitoring methods remains a critical challenge. Therefore, we developed an autoencoder...

A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders.

Journal of computational biology : a journal of computational molecular cell biology
Cancer is a disease that is both complex and diverse, and effective diagnosis and treatment require an accurate depiction of tumor subtypes. Traditional methods of cancer identification, which rely on clinical and histopathological criteria, have lim...

Uncertainty-based cardiac image registration using variational autoencoder with nonuniformly spaced control points.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plague...

Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification.

Computers in biology and medicine
Cardiac arrhythmias, characterized by irregular heart function, disrupt normal blood circulation and are commonly detected using electrocardiograms (ECGs). ECG is widely preferred due to its cost-effectiveness, ease of application, and high reliabili...

3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac anatomy and physiology vary considerably across the human population. Understanding and taking into account this variability is crucial for both accurate clinical decision-making and realistic in silico modeling of cardiac function. In this w...

VAE-GANMDA: A microbe-drug association prediction model integrating variational autoencoders and generative adversarial networks.

Artificial intelligence in medicine
Traditional biological experimental methods typically require weeks or even months of experimentation, and the cost of each experiment can reach hundreds or even thousands of dollars, which is quite expensive and time-consuming. To address this, a mo...

Noise-Consistent Hypergraph Autoencoder Based on Contrastive Learning for Cancer ceRNA Association Prediction in Complex Biological Regulatory Networks.

Journal of chemical information and modeling
Competitive endogenous RNA (ceRNA) regulatory networks (CENA) have advanced our understanding of noncoding RNAs' roles in complex diseases, providing a theoretical basis for disease mechanisms. Existing ceRNA-disease association prediction methods ar...

Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders.

Scientific reports
Relapse of psychotic disorders occurs commonly even after appropriate treatment. Digital phenotyping becomes essential to achieve remote monitoring for mental conditions. We applied a personalized approach using neural-network-based anomaly detection...