We have presented a graph variational auto-encoder based model for predicting genetic interactions, cell line dependencies and drug sensitivities. The model propagates gene ontology information over a ...

To address this, we propose a new end-to-end graph clustering framework that integrates a GCN and a variational autoencoder (VAE) with a more efficient and reasonable fusion mechanism in the semantic ...

Detecting anomalies in graph-structured data is critical for identifying unusual patterns within complex systems, with applications spanning cybersecurity, fraud detection, and risk assessment. In ...

representation-learning variational-inference link-prediction graph-convolutional-networks variational-autoencoder variational-autoencoders graph-embedding graph-neural-networks ...

Fu, Y., Yang, R. and Zhang, L. (2022) Association Prediction of Circrnas and Diseases Using Multi-Homogeneous Graphs and Variational Graph Auto-Encoder. Computers in Biology and Medicine, 151, Article ...

Keywords Variational Auto-Encoder, Speeded-Up Robust Features Hybrd Model Share and Cite: Kingori, S. , Nderu, L. and Njagi, D. (2025) Variational Auto-Encoder and Speeded-Up Robust Features Hybrd ...