ArticleScientific reports2025
pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting dephosphorylation site.Journal of molecular modeling · 2026Article
- Enhancing strawberry maturity assessment using mid-infrared spectral analysis with advanced variable selection and supervised classification.Scientific reports · 2026Article
- MetaPepticon: automated prediction of anticancer peptides from microbial genomes and metagenomes.PeerJ · 2026Article
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- Machine learning-based prediction of drug response in ischemia reperfusion animal model.Scientific reports · 2025Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
- Targeting neurodegeneration: three machine learning methods for G9a inhibitors discovery using PubChem and scikit-learn.Journal of computer-aided molecular design · 2025Article
- Deciphering the MHC immunopeptidome of human cancers with Ligand.MHC atlas.Briefings in bioinformatics · 2025Article
- BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention.Scientific reports · 2025Article
- Towards precision oncology: a multi-level cancer classification system integrating liquid biopsy and machine learning.BioData mining · 2025Article
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- K-M LLM-pro: Physics-guided cross-modal adaptation for fine-grained spatiotemporal trajectory classification.PloS one · 2025Article
- NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.Bioinformatics advances · 2025Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects. Furthermore, keeping the significant outcomes of the existing computational models, we propose a highly reliable and effective model namely, pACP-HybDeep for the accurate prediction of anticancer peptides. In this model, training peptides are numerically encoded using an attention-based ProtBERT-BFD encoder to extract semantic features along with CTDT-based structural information. Furthermore, a k-nearest neighbor-based binary tree growth (BTG) algorithm is employed to select an optimal feature set from the multi-perspective vector. The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. Our proposed pACP-HybDeep model demonstrated a high training accuracy of 95.33%, and an AUC of 0.97. To validate the generalization capabilities of the model, our pACP-HybDeep model achieved accuracies of 94.92%, 92.26%, and 91.16% on independent datasets Ind-S1, Ind-S2, and Ind-S3, respectively. The demonstrated efficacy, and reliability of the pACP-HybDeep model using test datasets establish it as a valuable tool for researchers in academia and pharmaceutical drug design.
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