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.

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

Keywords: metabolic diseases, natural medicines, drug discovery, graph autoencoder, metabolite-disease associations Citation: Liao Q, Zhao W, Wang Z, Xu L, Yang K, Liu X and Zhang L (2025) Deciphering ...

Dynamic graphs are a type of time series data used to describe a variety of social interactions and other forms of human behavior. Long-term short-term (LSTM) neural networks, an industry standard ...

This project implements a real-time anomaly detection system using an LSTM Autoencoder to analyze continuous data streams. The system is designed to detect unusual patterns, such as exceptional values ...

The Brain2Images uses Long Short Term Memory (LSTM) autoencoder for converting multi-channel EEG signals into an image for 2D CNN model classification. For systems with dynamic behaviors, many ...