Perturbation-Response-Prediction / PRnetLinks
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
☆54Updated 7 months ago
Alternatives and similar repositories for PRnet
Users that are interested in PRnet are comparing it to the libraries listed below
Sorting:
- PerturbNet is a deep generative model that can predict the distribution of cell states induced by chemical or genetic perturbation☆34Updated this week
- Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.☆118Updated 5 months ago
- ☆70Updated last month
- A visible neural network model for drug response prediction☆144Updated last year
- Contextual AI models for single-cell protein biology☆86Updated 5 months ago
- A Deep Learning based Efficacy Prediction System for drug discovery☆66Updated 2 years ago
- Large language modeling applied to T-cell receptor (TCR) sequences.☆54Updated 3 years ago
- Modeling complex perturbations with CellFlow☆64Updated last week
- Deep Learning the T Cell Receptor Binding Specificity of Neoantigen☆82Updated 3 years ago
- Repository for Nicheformer: a foundation model for single-cell and spatial omics☆94Updated 3 months ago
- GeneCompass☆84Updated 3 weeks ago
- ☆19Updated 9 months ago
- Single-Cell (Perturbation) Model Library☆63Updated 2 weeks ago
- The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell leve…☆102Updated 11 months ago
- Evaluation suite for transcriptomic perturbation effect prediction models. Includes support for single-cell foundation models.☆26Updated 3 weeks ago
- ☆31Updated 5 months ago
- ☆10Updated last month
- A model developed for the generation of scRNA-seq data☆67Updated 4 months ago
- scPerturb: A resource and a python/R tool for single-cell perturbation data☆129Updated 4 months ago
- State is a machine learning model that predicts cellular perturbation response across diverse contexts☆241Updated this week
- Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data☆120Updated last month
- Codes for paper: Evaluating the Utilities of Large Language Models in Single-cell Data Analysis.☆71Updated last month
- Transformer for One-Stop Interpretable Cell-type Annotation☆142Updated last year
- Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.☆58Updated last year
- ☆52Updated 7 months ago
- ☆49Updated last year
- 📝 [Paper] TCRen: predicting TCR-peptide recognition based on residue-level pairwise statistical potential☆18Updated 3 months ago
- GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations☆272Updated 5 months ago
- Computational Optimization of DNA Activity (CODA)☆60Updated 3 months ago
- ☆72Updated 10 months ago