schumacherlab / STAPLERLinks
STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers) is a language model that uses a joint TCRab-peptide input to predict TCRab-peptide specificity.
☆28Updated 6 months ago
Alternatives and similar repositories for STAPLER
Users that are interested in STAPLER are comparing it to the libraries listed below
Sorting:
- python tools for TCR:peptide-MHC modeling and analysis☆82Updated last year
- ☆21Updated 3 years ago
- TCR validated database☆41Updated 6 months ago
- Deep-learning empowered prediction and generation of immunogenic epitopes for T cell immunity☆73Updated 2 years ago
- Large language modeling applied to T-cell receptor (TCR) sequences.☆57Updated 3 years ago
- Fast AlphaFold-Multimer based pipeline for Protein-Protein Interaction (PPI) screening☆39Updated last year
- HLA-I ligand predictor☆43Updated last month
- ☆45Updated 7 months ago
- ☆51Updated last year
- A visualization software for antibody multi sequence alignment☆21Updated last year
- CodonBert: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism.☆40Updated last year
- Cross-protein transfer learning for variant effect prediction☆20Updated 2 years ago
- ☆82Updated 2 years ago
- Predicting immunogenic peptide recognized by TCR through ensemble deep learning☆17Updated last year
- ERGO-II, an updated version of ERGO including more features for TCR-peptide binding prediction☆34Updated 3 years ago
- GraphPart, a data partitioning method for ML on biological sequences☆31Updated 2 years ago
- Code repository of "Deep generative design of RNA family sequences"☆38Updated last week
- ☆13Updated last year
- ☆91Updated last year
- HLA-II ligand predictor.☆45Updated 2 years ago
- Code and data to reproduce analyses in Biswas et al. (2020) "Low-N protein engineering with data-efficient deep learning".☆59Updated 4 years ago
- ☆25Updated last year
- VDJ assignment and antibody sequence annotation. Scalable from a single sequence to billions of sequences.☆44Updated 3 months ago
- TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning☆12Updated 2 years ago
- MaveDB API☆13Updated last week
- RiboNN: predicting translation efficiencies from mRNA sequences☆42Updated last month
- ☆36Updated 10 months ago
- Evolutionary velocity with protein language models☆94Updated 2 years ago
- Pipeline for de novo peptide sequencing (Novor, DeepNovo, SMSNet, PointNovo, Casanovo) and assembly with ALPS.☆42Updated last year
- Predict the structure of immune receptor proteins☆57Updated last year