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
Implements both unsupervised (VAE) and supervised deep learning architectures for TCR sequence classification and repertoire analysis, with support for paired alpha/beta chains, V/D/J gene features, and HLA allotype/supertype context. Built on TensorFlow 2.0+ with GPU acceleration, featuring ensemble inference across cross-validated models, motif discovery via multinomial regression, and adaptive subsampling for large repertoires. Integrates repertoire-level and sequence-level predictions with optional Monte Carlo dropout and sparsity-regularized latent representations.
About TCRpeg
jiangdada1221/TCRpeg
Deep autoregressive generative models capture the intrinsics embedded in t-cell receptor repertoires
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