Making Protein folding accessible to all!
- bioinformatics
- multiple-sequence-alignment
- protein-structure
- structure-prediction
Repository Index
77,852 visible repositories in the index
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The repository host gives no description.
- foundation-model
- gpt
- single-cell
Reference mapping for single-cell genomics
- single-cell
- deep-learning
- rna-seq-analysis
- data-integration
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
- histology
- wsi-images
- pan-cancer-analysis
- genomics
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
🏃 The go-to single-cell Foundation Model
- batch-correction
- cell-biology
- celltype-annotation
- denoising
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
The repository host gives no description.
The repository host gives no description.
Integrated pipeline for multiplexed image analysis
- multiplexed-imaging
- python
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications
- cell-annotation
- multiplexed-imaging
- mpif
High-Dimensional Gene Expression and Morphology Profiles of Cells across 28,000 Genetic and Chemical Perturbations
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
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
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.