jiangdada1221/TCRpeg
Deep autoregressive generative models capture the intrinsics embedded in t-cell receptor repertoires
This tool helps immunologists and bioinformatics researchers analyze T-cell receptor (TCR) repertoires. You can input raw TCR sequences to understand their underlying probability distributions, generate new, statistically similar TCR sequences, or get numerical representations of existing TCRs. This helps in studying immune responses, identifying disease biomarkers, and designing targeted immunotherapies.
No commits in the last 6 months. Available on PyPI.
Use this if you need to analyze the statistical properties of large TCR sequence datasets, generate synthetic TCR repertoires for research, or develop classifiers for immune-related conditions.
Not ideal if you are looking for a simple, out-of-the-box solution for basic sequence alignment or single-cell gene expression analysis without a focus on generative modeling of repertoires.
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Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
May 01, 2025
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