Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.
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Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
- epigenomics
- pattern-search
- data-visualization
- interactive-machine-learning
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
- cell-annotation
- multiplexed-imaging
- mpif
Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
- foundation-models
- gpt
- single-cell
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
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
Multigrate: multiomic data integration for single-cell genomics
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
- cancer
- foundation-model
- single-cell
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
Single-cell fluorescence microscopy data analysis
- microscopy
- fluorescence-microscopy-imaging
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
- bioinformatics
- cell-type-deconvolution
- computational-biology
- deconvolution
PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"
Data logistics system enabling real-time pathogen surveillance. Built for the Seattle Flu Study.
- postgres
- postgresql
- python
- infectious-disease
Official implementation of JAMIE, Joint variational Autoencoders for Multimodal Imputation and Embedding
- autoencoder
- imputation
- integration
- multimodal
scPair: Boosting single cell multimodal analysis by leveraging single cell atlases and implicit feature selection
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.
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.
gateway for segmentation data to Rstats & visualization with ggplot2 - in development
- visualization
- fluorescence-data
- ggplot
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.