Public articles linked to the same research event.
bioRxiv 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.
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.