AlejandroTL

Pinned Repositories

AMMM
Language:Python0 1 00
BSC_IPC
Language:Jupyter Notebook00
CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Language:Jupyter Notebook0 0 00
dcdfg
Language:Python0 0 00
Medulloblastoma-VAE
Language:HTML0 1 00
OpenData
Language:Jupyter Notebook0 1 00
pert_graph
Language:Jupyter Notebook01
projects
Embedded Systems Projects
Language:Python0 1 00
SingleCell-VAE-Interpretability
Language:Python0 1 00
Explainable_Synthetic_Data_Generation_Medulloblastoma
Explainable synthetic data generation for paediatric cancer research
Language:HTML1 4 00

AlejandroTL's Repositories

AlejandroTL/AMMM
Language:Python0 1 00
AlejandroTL/BSC_IPC
Language:Jupyter Notebook00
AlejandroTL/CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Language:Jupyter Notebook0 0 00
AlejandroTL/dcdfg
Language:Python0 0 00
AlejandroTL/Medulloblastoma-VAE
Language:HTML0 1 00
AlejandroTL/OpenData
Language:Jupyter Notebook0 1 00
AlejandroTL/pert_graph
Language:Jupyter Notebook01
AlejandroTL/projects
Embedded Systems Projects
Language:Python0 1 00
AlejandroTL/SingleCell-VAE-Interpretability
Language:Python0 1 00