Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
- foundation-models
- gpt
- single-cell
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High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
Tool for precise, interactive, fast and scalable FISH spot detection
Methods for Image-Based Cell Profiling
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
- single-cell
- scrna-seq
- scatac-seq
- deep-neural-networks
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning
Predicting Cell Health with Morphological Profiles
- carpenter-lab
- morphological-profiling
- crispr
- cell-painting
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
A flexible Julia toolkit for high-dimensional cellular profiles
- julia
- morphological-analysis
- high-content-screening
DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.
Analysis of cell migration in Igor Pro
- igor
- imagej
- cell-migration
- tracking
Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.
DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Python library for statistical analysis of mRNA and protein distributions in fluorescent microscopy images
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
GNN-based gene-selection deep learning model for drug response prediction