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.
☆184Updated 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☆157Updated last year
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆134Updated last year
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆143Updated last year
- Models and datasets for perturbational single-cell omics☆171Updated 3 years ago
- Single cell perturbation prediction☆334Updated last year
- ☆19Updated 2 years ago
- Assorted tools for interacting with .gct, .gctx files and other Connectivity Map (Broad Institute) data/tools☆140Updated 3 years ago
- Discovering novel cell types across heterogenous single-cell experiments☆123Updated 3 years ago
- GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations☆331Updated last year
- Biological Network Integration using Convolutions☆63Updated 2 years ago
- Images and other data from the JUMP Cell Painting Consortium☆182Updated 2 weeks ago
- Machine Learning for Genomics and Therapeutics Resources (Cell Patterns)☆80Updated 4 years ago
- Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors☆43Updated last year
- Comprehensive suite for evaluating perturbation prediction models☆122Updated 3 weeks ago
- P-NET, Biologically informed deep neural network for prostate cancer classification and discovery☆165Updated 4 years ago
- Formalizing and benchmarking open problems in single-cell genomics☆420Updated 2 months ago
- ☆90Updated last year
- A generative topic model that facilitates integrative analysis of large-scale single-cell RNA sequencing data.☆52Updated 4 years ago
- Single-cell perturbation analysis☆283Updated last week
- ☆66Updated 2 years ago
- ☆308Updated last year
- ☆81Updated 3 years ago
- UCE is a zero-shot foundation model for single-cell gene expression data☆242Updated 11 months ago
- Gene2Vec: Distributed Representation of Genes Based on Co-Expression☆129Updated 3 years ago
- ☆70Updated 6 months ago
- Variational Auto-Encoder that generates synthetic gene expression data☆25Updated 2 years ago
- CellBox: Interpretable Machine Learning for Perturbation Biology☆56Updated 2 years ago
- CpG Transformer for imputation of single-cell methylomes☆36Updated 2 years ago
- Training and evaluating a variational autoencoder for pan-cancer gene expression data☆173Updated 7 years ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆165Updated 11 months ago