ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
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Foundation Models for Genomics & Transcriptomics
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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
Repository for Nicheformer: a foundation model for single-cell and spatial omics
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
GeneCompass
Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
BulkFormer: A large-scale foundation model for human bulk transcriptomes
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
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
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network