Repository Index

Probabilistic cell segmentation for in situ spatial transcriptomics

scPerturb: A resource and a python/R tool for single-cell perturbation data

• cite-seq
• database
• perturbation
• scatac-seq

Repository for Nicheformer: a foundation model for single-cell and spatial omics

PhyloNetworks: a julia package with utilities for phylogenetic networks

LLM for Drug Editing, ICLR 2024

• chatgpt
• conversation
• drug
• drug-discovery

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

• self-attention
• compound-protein-interaction
• transformercpi
• structure-free-virtual-sreening

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design

• protein
• protein-sequences
• protein-design
• representation-learning

Rails frontend to The Genome Institute's drug gene interaction database.

PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).

• deep-generative-model
• deep-learning
• single-cell-data-integration
• single-cell-multiomics

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

The repository host gives no description.

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

Predicting Cell Health with Morphological Profiles

• carpenter-lab
• morphological-profiling
• crispr
• cell-painting

Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network

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

Single-cell fluorescence microscopy data analysis

• microscopy
• fluorescence-microscopy-imaging

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

A flexible Julia toolkit for high-dimensional cellular profiles

• julia
• morphological-analysis
• high-content-screening

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.