{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:54:17Z","timestamp":1783551257958,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013489","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000}}],"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302198"],"award-info":[{"award-number":["62302198"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Funded by Basic Research Program of Jiangsu","award":["BK20231035"],"award-info":[{"award-number":["BK20231035"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JUSRP124014"],"award-info":[{"award-number":["JUSRP124014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["SJCX25_1317"],"award-info":[{"award-number":["SJCX25_1317"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Cancer remains a major contributor to global mortality, constituting a significant and escalating threat to human health. Anticancer peptides (ACPs) have emerged as promising therapeutic agents due to their specific mechanisms of action, pronounced tumor-targeting capability, and low toxicity. Nevertheless, traditional approaches for ACP identification are constrained by their reliance on shallow, hand-crafted sequence features, which fail to capture deeper semantic and structural characteristics. Moreover, such models exhibit limited robustness and interpretability when confronted with practical challenges such as severe class imbalance. To address these limitations, this study proposes HyperACP, an innovative framework for ACP recognition that integrates deep representation learning, adaptive sampling, and mechanistic interpretability. The framework leverages the ESMC protein language model to extract comprehensive sequence features and employs a novel adaptive algorithm, ANBS, to mitigate class imbalance at the decision boundary. For enhanced model transparency, SHAP-Res is incorporated to elucidate the contributions of individual residues to the final predictions. Comprehensive evaluations demonstrate that HyperACP consistently outperforms state-of-the-art methods across multiple datasets and validation protocols\u2014including 10-fold cross-validation and independent test sets\u2014according to metrics such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC). Furthermore, the model yields biologically interpretable results, pinpointing key residues (K, L, F, G) known to play pivotal roles in anticancer activity. These findings provide not only a robust predictive tool (available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.hyperacp.com\" xlink:type=\"simple\">www.hyperacp.com<\/jats:ext-link>) but also novel insights into the structure-function relationships underlying ACPs.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013489","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T17:43:08Z","timestamp":1757612588000},"page":"e1013489","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["HyperACP: A cutting-edge hybrid framework for anticancer peptide classification via scalable feature extraction and adaptive neighbor-based synthesis"],"prefix":"10.1371","volume":"21","author":[{"given":"Bangyi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4021-3836","authenticated-orcid":true,"given":"Yun","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangrong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohong","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"issue":"1","key":"pcbi.1013489.ref001","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"RL Siegel","year":"2024","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"pcbi.1013489.ref002","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"RL Siegel","year":"2023","journal-title":"CA Cancer J Clin"},{"key":"pcbi.1013489.ref003","doi-asserted-by":"crossref","first-page":"122498","DOI":"10.1016\/j.eswa.2023.122498","article-title":"Geometric deep learning for drug discovery","volume":"240","author":"M Liu","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pcbi.1013489.ref004","doi-asserted-by":"crossref","first-page":"114996","DOI":"10.1016\/j.biopha.2023.114996","article-title":"Current research status of anti-cancer peptides: Mechanism of action, production, and clinical applications","volume":"164","author":"RK Chinnadurai","year":"2023","journal-title":"Biomed Pharmacother"},{"issue":"6","key":"pcbi.1013489.ref005","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1038\/s12276-023-01016-x","article-title":"Peptides as multifunctional players in cancer therapy","volume":"55","author":"SMP Vadevoo","year":"2023","journal-title":"Exp Mol Med"},{"issue":"5","key":"pcbi.1013489.ref006","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-025-11148-3","article-title":"Bridging machine learning and peptide design for cancer treatment: a comprehensive review","volume":"58","author":"K Rezaee","year":"2025","journal-title":"Artif Intell Rev"},{"issue":"1","key":"pcbi.1013489.ref007","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/TCBB.2013.146","article-title":"Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set","volume":"11","author":"L Wei","year":"2014","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"4","key":"pcbi.1013489.ref008","doi-asserted-by":"crossref","first-page":"221","DOI":"10.2174\/0113894501322734241008163304","article-title":"Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives","volume":"26","author":"M Mohapatra","year":"2024","journal-title":"Curr Drug Targets"},{"issue":"6","key":"pcbi.1013489.ref009","article-title":"BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo","volume":"19","author":"H Li","year":"2023","journal-title":"PLoS Comput Biol"},{"issue":"11","key":"pcbi.1013489.ref010","article-title":"Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction","volume":"10","author":"Y Jiang","year":"2023","journal-title":"Adv Sci (Weinh)"},{"issue":"7","key":"pcbi.1013489.ref011","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/nar\/gkad055","article-title":"DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis","volume":"51","author":"R Wang","year":"2023","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"pcbi.1013489.ref012","doi-asserted-by":"crossref","first-page":"2362","DOI":"10.1109\/JBHI.2024.3357979","article-title":"Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering","volume":"28","author":"G Li","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"issue":"10","key":"pcbi.1013489.ref013","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.3390\/molecules24101973","article-title":"ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides","volume":"24","author":"N Schaduangrat","year":"2019","journal-title":"Molecules"},{"issue":"1","key":"pcbi.1013489.ref014","doi-asserted-by":"crossref","first-page":"21915","DOI":"10.1038\/s41598-022-24404-1","article-title":"ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction","volume":"12","author":"B Han","year":"2022","journal-title":"Sci Rep"},{"issue":"17","key":"pcbi.1013489.ref015","doi-asserted-by":"crossref","first-page":"168687","DOI":"10.1016\/j.jmb.2024.168687","article-title":"mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations","volume":"436","author":"VK Sangaraju","year":"2024","journal-title":"J Mol Biol"},{"key":"pcbi.1013489.ref016","doi-asserted-by":"crossref","first-page":"108063","DOI":"10.1016\/j.compbiomed.2024.108063","article-title":"ACP-ML: A sequence-based method for anticancer peptide prediction","volume":"170","author":"J Bian","year":"2024","journal-title":"Comput Biol Med"},{"key":"pcbi.1013489.ref017","first-page":"6492","volume-title":"Mitigating world biases: A multimodal multi-view debiasing framework for fake news video detection","author":"Z Zeng","year":"2024"},{"issue":"1","key":"pcbi.1013489.ref018","doi-asserted-by":"crossref","first-page":"13594","DOI":"10.1038\/s41598-021-93124-9","article-title":"Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties","volume":"11","author":"K-Y Huang","year":"2021","journal-title":"Sci Rep"},{"key":"pcbi.1013489.ref019","doi-asserted-by":"crossref","first-page":"1291352","DOI":"10.3389\/fmed.2023.1291352","article-title":"Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings","volume":"10","author":"H Zulfiqar","year":"2024","journal-title":"Front Med (Lausanne)"},{"issue":"14","key":"pcbi.1013489.ref020","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.3390\/diagnostics13142465","article-title":"A First Computational Frame for Recognizing Heparin-Binding Protein","volume":"13","author":"W Zhu","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"pcbi.1013489.ref021","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.ins.2016.06.026","article-title":"Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information","volume":"384","author":"L Wei","year":"2017","journal-title":"Information Sciences"},{"key":"pcbi.1013489.ref022","author":"GA Pradipta","year":"2021","journal-title":"SMOTE for handling imbalanced data problem: A review"},{"key":"pcbi.1013489.ref023","volume-title":"A comparative review of SMOTE and ADASYN in imbalanced data classification","author":"J Brandt","year":"2021"},{"issue":"3","key":"pcbi.1013489.ref024","doi-asserted-by":"crossref","DOI":"10.1002\/imt2.191","article-title":"SeqKit2: A Swiss army knife for sequence and alignment processing","volume":"3","author":"W Shen","year":"2024","journal-title":"Imeta"},{"key":"pcbi.1013489.ref025","unstructured":"EvolutionaryScale T. EvolutionaryScale T. evolutionaryscale\/esm. 2024. https:\/\/zenodo.org\/record\/25"},{"key":"pcbi.1013489.ref026","author":"T Hayes","year":"2024","journal-title":"Simulating 500 million years of evolution with a language model. Cold Spring Harbor Laboratory"},{"key":"pcbi.1013489.ref027","volume-title":"ESM Cambrian: Revealing the mysteries of proteins with unsupervised learning","author":"Team ESM","year":"2024"},{"key":"pcbi.1013489.ref028","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/978-3-030-56485-8_3","article-title":"Random forests","volume-title":"Random forests with R","author":"R Genuer","year":"2020"},{"issue":"3","key":"pcbi.1013489.ref029","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"C Bent\u00e9jac","year":"2021","journal-title":"Artif Intell Rev"},{"key":"pcbi.1013489.ref030","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-3-030-37334-4_4","article-title":"Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees","author":"A Guryanov","year":"2019","journal-title":"Analysis of Images, Social Networks and Texts"},{"key":"pcbi.1013489.ref031","doi-asserted-by":"crossref","first-page":"1281880","DOI":"10.3389\/fmed.2023.1281880","article-title":"Accurately identifying hemagglutinin using sequence information and machine learning methods","volume":"10","author":"X Zou","year":"2023","journal-title":"Front Med (Lausanne)"},{"issue":"7","key":"pcbi.1013489.ref032","article-title":"Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching","volume":"11","author":"X Guo","year":"2024","journal-title":"Adv Sci (Weinh)"},{"issue":"1","key":"pcbi.1013489.ref033","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1186\/s12915-024-02085-8","article-title":"Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data","volume":"22","author":"Z Huang","year":"2024","journal-title":"BMC Biol"},{"issue":"1","key":"pcbi.1013489.ref034","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1186\/s12915-024-01968-0","article-title":"Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation","volume":"22","author":"H Zhu","year":"2024","journal-title":"BMC Biol"},{"issue":"4","key":"pcbi.1013489.ref035","article-title":"RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing","volume":"4","author":"F Dao","year":"2025","journal-title":"Imeta"},{"issue":"11","key":"pcbi.1013489.ref036","article-title":"TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning","volume":"67","author":"K Yan","year":"2024","journal-title":"Sci China Inf Sci"},{"issue":"22","key":"pcbi.1013489.ref037","article-title":"BioSeq-BLM: a platform for analyzing DNA, RNA and protein sequences based on biological language models","volume":"49","author":"HL Li","year":"2021","journal-title":"Nucleic Acids Research"},{"issue":"10","key":"pcbi.1013489.ref038","first-page":"4663","article-title":"Challenges in KNN classification","volume":"34","author":"K Zhang SJIT o","year":"2021","journal-title":"Journal of Engineering"},{"issue":"7","key":"pcbi.1013489.ref039","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1038\/nmeth.3904","article-title":"Logistic regression","volume":"13","author":"J Lever","year":"2016","journal-title":"Nat Methods"},{"issue":"2","key":"pcbi.1013489.ref040","first-page":"130","article-title":"Decision tree methods: applications for classification and prediction","volume":"27","author":"Y-Y Song","year":"2015","journal-title":"Shanghai Arch Psychiatry"},{"key":"pcbi.1013489.ref041","doi-asserted-by":"crossref","first-page":"106760","DOI":"10.1016\/j.jff.2025.106760","article-title":"Exploring the anticancer potential of cricket-derived peptides in human cancer cells; pro-apoptotic effects via a caspase-3 pathway","volume":"127","author":"R Summart","year":"2025","journal-title":"Journal of Functional Foods"},{"issue":"1","key":"pcbi.1013489.ref042","first-page":"48","article-title":"Antimicrobial peptides: mechanism of action, activity and clinical potential","volume":"8","author":"Q-Y Zhang","year":"2021","journal-title":"Mil Med Res"},{"key":"pcbi.1013489.ref043","doi-asserted-by":"crossref","first-page":"106993","DOI":"10.1016\/j.knosys.2021.106993","article-title":"Interpretable machine learning with an ensemble of gradient boosting machines","volume":"222","author":"AV Konstantinov","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"pcbi.1013489.ref044","doi-asserted-by":"crossref","first-page":"1261889","DOI":"10.3389\/fmicb.2023.1261889","article-title":"Machine learning approaches in microbiome research: challenges and best practices","volume":"14","author":"G Papoutsoglou","year":"2023","journal-title":"Front Microbiol"},{"issue":"1","key":"pcbi.1013489.ref045","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41392-024-02107-5","article-title":"Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines","volume":"10","author":"W Xiao","year":"2025","journal-title":"Signal Transduct Target Ther"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1013489","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013489","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T17:50:42Z","timestamp":1758131442000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013489"}},"subtitle":[],"editor":[{"given":"Lun","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":45,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013489","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1013489","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]}}}