Repository Index

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

Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.

• foundation-models
• gpt
• single-cell

Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.

• analysis
• claude
• claudecode
• dna

Tool for precise, interactive, fast and scalable FISH spot detection

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

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

3D shape analysis using deep learning

• cancer-biology
• cell-biology
• deep-learning
• geometric-deep-learning

A versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics

• bioinformatics
• cell-type-deconvolution
• computational-biology
• deconvolution

Official implementation of JAMIE, Joint variational Autoencoders for Multimodal Imputation and Embedding

• autoencoder
• imputation
• integration
• multimodal

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

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

GNN-based gene-selection deep learning model for drug response prediction