Public articles linked to the same research event.
bioRxiv The study presents OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records that uses sequence-type tokens and complementary component orders to jointly learn from individual TCR chains and partial or complete TCR-pMHC associations, thereby performing both TCR recognition prediction and conditional sequence generation within one model, achieving AUPRCs of 0.7009 for peptide-TCRβ recognition and 0.8235 for TCR-pMHC interaction prediction on unseen epitopes and a mean AUROC of 0.9436 in distinguishing cancer from healthy repertoires across 11 independent pan-cancer cohorts.
The study presents OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records that uses sequence-type tokens and complementary component orders to jointly learn from individual TCR chains and partial or complete TCR-pMHC associations, thereby performing both TCR recognition prediction and conditional sequence generation within one model, achieving AUPRCs of 0.7009 for peptide-TCRβ recognition and 0.8235 for TCR-pMHC interaction prediction on unseen epitopes and a mean AUROC of 0.9436 in distinguishing cancer from healthy repertoires across 11 independent pan-cancer cohorts.
The study presents OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records that uses sequence-type tokens and complementary component orders to jointly learn from individual TCR chains and partial or complete TCR-pMHC associations, thereby performing both TCR recognition prediction and conditional sequence generation within one model, achieving AUPRCs of 0.7009 for peptide-TCRβ recognition and 0.8235 for TCR-pMHC interaction prediction on unseen epitopes and a mean AUROC of 0.9436 in distinguishing cancer from healthy repertoires across 11 independent pan-cancer cohorts.
The study presents OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records that uses sequence-type tokens and complementary component orders to jointly learn from individual TCR chains and partial or complete TCR-pMHC associations, thereby performing both TCR recognition prediction and conditional sequence generation within one model, achieving AUPRCs of 0.7009 for peptide-TCRβ recognition and 0.8235 for TCR-pMHC interaction prediction on unseen epitopes and a mean AUROC of 0.9436 in distinguishing cancer from healthy repertoires across 11 independent pan-cancer cohorts.