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.
Repository Index
47,133 repositories indexed
1–15 of 15 · Most stars
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
- drug-repurposing
- deep-learning
- drug-target-interactions
- toolkit
Precision Medicine Knowledge Graph (PrimeKG)
- knowledge-graph
- precision-medicine
- therapeutics
- bioinformatics
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
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
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
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
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
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.