Second, the applied deep learning method is based on an autoencoder where a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is utilized as the autoencoder ...

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

AI-driven automation is revolutionizing data engineering, enabling businesses to optimize, scale and enhance data ecosystems. Agentic mesh architecture can improve resilience and adaptability.

Figure 1. Basic Autoencoder architecture, showing encoder and decoder components [22]. Figure 2. AE-based framework for signal reconstruction, highlighting latent space compression. Recent ...

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon ...

In this viewpoint, we briefly review recently developed autoencoder-based models designed to enhance the conformational exploration of IDPs through embedding and latent sampling.

GitHub is where people build software. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects.