Life Sciences

193 items

bioRxiv Sep 13, 2026
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.
OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation
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.
bioRxiv Sep 13, 2026
This work proposes ICARus, a positive-unlabelled read-out refinement framework that integrates protein-protein interaction information derived from a protein language model into a genome-wide RNA interference screen to boost the discovery of vaccinia virus-host interactions and human genes with potential antiviral function, and releases the raw and refined read-outs of the screen as an agentic-AI-enabled community resource.
This work proposes ICARus, a positive-unlabelled read-out refinement framework that integrates protein-protein interaction information derived from a protein language model into a genome-wide RNA interference screen to boost the discovery of vaccinia virus-host interactions and human genes with potential antiviral function, and releases the raw and refined read-outs of the screen as an agentic-AI-enabled community resource.
Agentic-AI-ready genome-wide poxvirus-host interaction screen refined by a protein language model
This work proposes ICARus, a positive-unlabelled read-out refinement framework that integrates protein-protein interaction information derived from a protein language model into a genome-wide RNA interference screen to boost the discovery of vaccinia virus-host interactions and human genes with potential antiviral function, and releases the raw and refined read-outs of the screen as an agentic-AI-enabled community resource.
This work proposes ICARus, a positive-unlabelled read-out refinement framework that integrates protein-protein interaction information derived from a protein language model into a genome-wide RNA interference screen to boost the discovery of vaccinia virus-host interactions and human genes with potential antiviral function, and releases the raw and refined read-outs of the screen as an agentic-AI-enabled community resource.
bioRxiv Sep 13, 2026
This work presents ABCP_finder, a computational framework for predicting anti-breast cancer peptides (ABCPs) that combines pretrained protein language model embeddings (ProtBERT and ESM2) with a multilayer perceptron classifier, uses a homology-aware train-test split via CD-HIT at 30% sequence identity with 80% coverage to reduce data leakage, reports ProtBERT as the stronger model with 93.82% accuracy, 86.88% recall, 90.59% F1-score, 0.8618 MCC, 96.67% AUC and a Brier score of 0.0633, selects a 0.7 probability threshold from calibration analysis for high-confidence ABCPs, and shows through external validation with xDeep-AcPEP that unknown peptides predicted as ABCPs exhibit favourable IC values.
This work presents ABCP_finder, a computational framework for predicting anti-breast cancer peptides (ABCPs) that combines pretrained protein language model embeddings (ProtBERT and ESM2) with a multilayer perceptron classifier, uses a homology-aware train-test split via CD-HIT at 30% sequence identity with 80% coverage to reduce data leakage, reports ProtBERT as the stronger model with 93.82% accuracy, 86.88% recall, 90.59% F1-score, 0.8618 MCC, 96.67% AUC and a Brier score of 0.0633, selects a 0.7 probability threshold from calibration analysis for high-confidence ABCPs, and shows through external validation with xDeep-AcPEP that unknown peptides predicted as ABCPs exhibit favourable IC values.
ABCP_finder: A Transformer Embedding-Based Prediction of Anti-Breast Cancer Peptides
This work presents ABCP_finder, a computational framework for predicting anti-breast cancer peptides (ABCPs) that combines pretrained protein language model embeddings (ProtBERT and ESM2) with a multilayer perceptron classifier, uses a homology-aware train-test split via CD-HIT at 30% sequence identity with 80% coverage to reduce data leakage, reports ProtBERT as the stronger model with 93.82% accuracy, 86.88% recall, 90.59% F1-score, 0.8618 MCC, 96.67% AUC and a Brier score of 0.0633, selects a 0.7 probability threshold from calibration analysis for high-confidence ABCPs, and shows through external validation with xDeep-AcPEP that unknown peptides predicted as ABCPs exhibit favourable IC values.
This work presents ABCP_finder, a computational framework for predicting anti-breast cancer peptides (ABCPs) that combines pretrained protein language model embeddings (ProtBERT and ESM2) with a multilayer perceptron classifier, uses a homology-aware train-test split via CD-HIT at 30% sequence identity with 80% coverage to reduce data leakage, reports ProtBERT as the stronger model with 93.82% accuracy, 86.88% recall, 90.59% F1-score, 0.8618 MCC, 96.67% AUC and a Brier score of 0.0633, selects a 0.7 probability threshold from calibration analysis for high-confidence ABCPs, and shows through external validation with xDeep-AcPEP that unknown peptides predicted as ABCPs exhibit favourable IC values.

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OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation

Agentic-AI-ready genome-wide poxvirus-host interaction screen refined by a protein language model

ABCP_finder: A Transformer Embedding-Based Prediction of Anti-Breast Cancer Peptides