DeepTCR and TCRpeg
These are complementary tools: DeepTCR focuses on discriminative analysis and classification of TCR sequences, while TCRpeg provides generative modeling of TCR repertoires, enabling different downstream applications (prediction vs. generation) on the same data type.
About DeepTCR
sidhomj/DeepTCR
Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data
This tool helps immunologists and genetic researchers analyze T-Cell Receptor (TCR) sequencing data. It takes raw TCR sequences, including paired alpha/beta chains, V/D/J gene usage, and associated HLA information, to identify patterns. The output helps understand T-cell repertoires and their association with various biological conditions, providing insights into immune responses.
About TCRpeg
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.
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