facebookresearch / CPALinks
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.
☆182Updated 2 years ago
Alternatives and similar repositories for CPA
Users that are interested in CPA are comparing it to the libraries listed below
Sorting:
- Learning Single-Cell Perturbation Responses using Neural Optimal Transport☆150Updated last year
- Models and datasets for perturbational single-cell omics☆167Updated 3 years ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆118Updated last year
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆129Updated 9 months ago
- Single cell perturbation prediction☆325Updated 11 months ago
- Images and other data from the JUMP Cell Painting Consortium☆178Updated this week
- GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations☆309Updated 9 months ago
- ☆19Updated 2 years ago
- ☆63Updated 2 years ago
- Comprehensive suite for evaluating perturbation prediction models☆106Updated last week
- Batch-adversarial variational auto-encoder (BAVARIA) for simultaneous dimensionality reduction and integration of single-cell ATAC-seq da…☆14Updated 2 years ago
- CpG Transformer for imputation of single-cell methylomes☆37Updated 2 years ago
- Single-cell perturbation analysis☆213Updated last week
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆53Updated 3 years ago
- Biological Network Integration using Convolutions☆62Updated last year
- ☆295Updated last year
- Gene2Vec: Distributed Representation of Genes Based on Co-Expression☆125Updated 3 years ago
- Formalizing and benchmarking open problems in single-cell genomics☆412Updated 3 months ago
- Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors☆42Updated last year
- Discovering novel cell types across heterogenous single-cell experiments☆123Updated 2 years ago
- ☆80Updated 3 years ago
- ☆86Updated last year
- ☆18Updated 3 years ago
- CellBox: Interpretable Machine Learning for Perturbation Biology☆56Updated 2 years ago
- Learning cell communication from spatial graphs of cells☆114Updated last year
- UCE is a zero-shot foundation model for single-cell gene expression data☆224Updated 8 months ago
- P-NET, Biologically informed deep neural network for prostate cancer classification and discovery☆162Updated 4 years ago
- Evaluation suite for transcriptomic perturbation effect prediction models. Includes support for single-cell foundation models.☆29Updated 3 months ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆152Updated 8 months ago
- Training and evaluating a variational autoencoder for pan-cancer gene expression data☆172Updated 6 years ago