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- foundation-model
- gpt
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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.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Foundation Models for Genomics & Transcriptomics
Precision Medicine Knowledge Graph (PrimeKG)
Reference mapping for single-cell genomics
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
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Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
scPerturb: A resource and a python/R tool for single-cell perturbation data
Repository for Nicheformer: a foundation model for single-cell and spatial omics
🏃 The go-to single-cell Foundation Model
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
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GeneCompass
Integrated pipeline for multiplexed image analysis
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
Machine learning for Analysis of Proteomics in Spatial biology - Nature Communications