AESTETIK
Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.
- from
- Computational Biology
- added
- 2026-10-10
- likes
- 0
similar
-
STAGATE github.com
Adaptive graph attention auto-encoder for spatial domain identification in spatial transcriptomics.
-
DeepSpot github.com
Deep learning model predicting spatial transcriptomics from H&E images at spot and single-cell resolution.
-
DeepSpot-M github.com
Multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology.
-
SpatialGlue github.com
Graph attention network for spatial multi-omics integration jointly embedding spatial transcriptomics with chromatin accessibility or proteomics.
-
DeepSpot2Cell github.com
Predicts virtual single-cell spatial transcriptomics from H&E using spot-level supervision (NeurIPS 2025 Imageomics).
-
DeepTalk github.com
Graph attention network for deciphering cell-cell communication from spatial transcriptomics.
Computational Biology › Machine Learning Tasks and Models > Foundation Models > Single-cell Foundation Models: “Autoencoder for spatial transcriptomics representation learning using topology and histology image knowledge.”