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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Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena
- foundation-models
- genomics
- language-models
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
- kmer
- dnabert-model
- kmer-format
- gpu
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
Official release of the ProGen models
- language-model
- protein
- generative-model
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
- dataset
- dna
- dna-processing
- dna-training
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
Ankh: Optimized Protein Language Model
🏃 The go-to single-cell Foundation Model
- batch-correction
- cell-biology
- celltype-annotation
- denoising
Official repo for CellPLM: Pre-training of Cell Language Model Beyond Single Cells.
Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
- foundation-models
- gpt
- single-cell
MCP server for spatial transcriptomics analysis through natural language interfaces.
- bioinformatics
- cell-communication
- deconvolution
- machine-learning