{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T19:52:53Z","timestamp":1780516373352,"version":"3.54.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T00:00:00Z","timestamp":1709769600000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12101480"],"award-info":[{"award-number":["12101480"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2024JC-YBMS-004"],"award-info":[{"award-number":["2024JC-YBMS-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae142","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T15:00:42Z","timestamp":1709737242000},"source":"Crossref","is-referenced-by-count":12,"title":["AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8786-0940","authenticated-orcid":false,"given":"Shengli","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University , Xi'an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University , Xi'an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2019an Polytechnic University , Xi'an 710048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,7]]},"reference":[{"key":"2024032808412310500_btae142-B1","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.3390\/ijms20081964","article-title":"mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides","volume":"20","author":"Boopathi","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2024032808412310500_btae142-B2","first-page":"28","article-title":"Fitting a mixture model by expectation maximization to discover motifs in biopolymers","volume":"2","author":"Bailey","year":"1994","journal-title":"Proc Int Conf Intell Syst Mol Biol"},{"key":"2024032808412310500_btae142-B3","first-page":"bbz043","article-title":"Characterization and identification of antimicrobial peptides with different functional activities","volume":"6","author":"Chung","year":"2019","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ygeno.2015.12.003","article-title":"20D-dynamic representation of protein sequences","volume":"107","author":"Czerniecka","year":"2016","journal-title":"Genomics"},{"key":"2024032808412310500_btae142-B5","doi-asserted-by":"crossref","first-page":"bbac606","DOI":"10.1093\/bib\/bbac606","article-title":"AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning","volume":"24","author":"Fang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B6","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s00894-019-4007-6","article-title":"De novo design of anticancer peptides by ensemble artificial neural networks","volume":"25","author":"Grisoni","year":"2019","journal-title":"J Mol Model"},{"key":"2024032808412310500_btae142-B7","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1021\/acs.jnatprod.1c00222","article-title":"The total chemical synthesis and biological evaluation of the cationic antimicrobial peptides, laterocidine and brevicidine","volume":"84","author":"Hermant","year":"2021","journal-title":"J Nat Prod"},{"key":"2024032808412310500_btae142-B8","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1158\/1535-7163.MCT-10-0811","article-title":"Studies on mechanism of action of anticancer peptides by modulation of hydrophobicity within a defined structural framework","volume":"10","author":"Huang","year":"2011","journal-title":"Mol Cancer Ther"},{"key":"2024032808412310500_btae142-B9","doi-asserted-by":"crossref","first-page":"bbad246","DOI":"10.1093\/bib\/bbad246","article-title":"DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins","volume":"24","author":"He","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.2174\/1574893611666160609081155","article-title":"Cancer diagnosis through IsomiR expression with machine learning method","volume":"13","author":"Liao","year":"2018","journal-title":"CBIO"},{"key":"2024032808412310500_btae142-B11","doi-asserted-by":"crossref","first-page":"bbab008","DOI":"10.1093\/bib\/bbab008","article-title":"Anticancer peptides prediction with deep representation learning features","volume":"22","author":"Lv","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B12","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1093\/bioinformatics\/btaa1074","article-title":"Identification of Sub-Golgi protein localization by use of deep representation learning features","volume":"36","author":"Lv","year":"2021","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B13","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1093\/bioinformatics\/btac532","article-title":"An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites","volume":"38","author":"Li","year":"2022","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B14","doi-asserted-by":"crossref","first-page":"bbac579","DOI":"10.1093\/bib\/bbac579","article-title":"LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism","volume":"24","author":"Liu","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B15","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s12539-023-00572-0","article-title":"CRBP-HFEF: prediction of RBP-Binding sites on circRNAs based on hierarchical feature expansion and fusion","volume":"15","author":"Ma","year":"2023","journal-title":"Interdiscip Sci"},{"key":"2024032808412310500_btae142-B16","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.3390\/cells8111332","article-title":"4mCpred-EL: an ensemble learning framework for identification of DNA N4-methylcytosine sites in the mouse genome","volume":"8","author":"Manavalan","year":"2019","journal-title":"Cells"},{"key":"2024032808412310500_btae142-B17","doi-asserted-by":"crossref","first-page":"107770","DOI":"10.1016\/j.compbiolchem.2022.107770","article-title":"DeeProPre: a promoter predictor based on deep learning","volume":"101","author":"Ma","year":"2022","journal-title":"Comput Biol Chem"},{"key":"2024032808412310500_btae142-B18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1080\/10629360802085066","article-title":"Representation of proteins as walks in 20-D space","volume":"19","author":"Novic","year":"2008","journal-title":"SAR QSAR Environ Res"},{"key":"2024032808412310500_btae142-B19","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1093\/bioinformatics\/btv345","article-title":"ProFET: feature engineering captures high-level protein functions","volume":"31","author":"Ofer","year":"2015","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B20","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1093\/bib\/bbz088","article-title":"ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides","volume":"21","author":"Rao","year":"2020","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B21","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.drudis.2018.05.024","article-title":"Host-defense peptides and their potential use as biomarkers in human diseases","volume":"23","author":"Silva","year":"2018","journal-title":"Drug Discov Today"},{"key":"2024032808412310500_btae142-B22","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":"Schaduangrat","year":"2019","journal-title":"Molecules"},{"key":"2024032808412310500_btae142-B23","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1093\/bioinformatics\/btaa003","article-title":"UDSMProt: universal deep sequence models for protein classification","volume":"36","author":"Strodthoff","year":"2020","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B24","doi-asserted-by":"crossref","first-page":"bbac341","DOI":"10.1093\/bib\/bbac341","article-title":"R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting","volume":"23","author":"Shi","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B25","doi-asserted-by":"crossref","first-page":"D837","DOI":"10.1093\/nar\/gku892","article-title":"CancerPPD: a database of anticancer peptides and proteins","volume":"43","author":"Tyagi","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2024032808412310500_btae142-B26","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1038\/srep02984","article-title":"In silico models for designing and discovering novel anticancer peptides","volume":"3","author":"Tyagi","year":"2013","journal-title":"Sci Rep"},{"key":"2024032808412310500_btae142-B27","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1186\/s12859-021-03965-4","article-title":"Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides","volume":"22","author":"Wan","year":"2021","journal-title":"BMC Bioinformatics"},{"key":"2024032808412310500_btae142-B28","doi-asserted-by":"crossref","first-page":"bbab226","DOI":"10.1093\/bib\/bbab226","article-title":"DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19","volume":"22","author":"Wang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B29","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1093\/bioinformatics\/btac374","article-title":"Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks","volume":"38","author":"Wang","year":"2022","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B30","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TCBB.2020.2982873","article-title":"ncRFP: a novel end-to-end method for non-coding RNAs family prediction based on deep learning","volume":"18","author":"Wang","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024032808412310500_btae142-B31","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3390\/genes9030158","article-title":"A novel hybrid Sequence-Based model for identifying anticancer peptides","volume":"9","author":"Xu","year":"2018","journal-title":"Genes (Basel)"},{"key":"2024032808412310500_btae142-B32","doi-asserted-by":"crossref","first-page":"btad436","DOI":"10.1093\/bioinformatics\/btad436","article-title":"A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network","volume":"39","author":"Xin","year":"2023","journal-title":"Bioinformatics"},{"key":"2024032808412310500_btae142-B33","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/s12539-021-00481-0","article-title":"Anti cancer peptide recognition based on grouped sequence and spatial dimension integrated networks","volume":"14","author":"You","year":"2022","journal-title":"Interdiscip Sci"},{"key":"2024032808412310500_btae142-B34","doi-asserted-by":"crossref","first-page":"bbac630","DOI":"10.1093\/bib\/bbac630","article-title":"Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding","volume":"24","author":"Yuan","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B35","doi-asserted-by":"crossref","first-page":"bbad036","DOI":"10.1093\/bib\/bbad036","article-title":"Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction","volume":"24","author":"Yu","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B36","doi-asserted-by":"crossref","first-page":"bbad095","DOI":"10.1093\/bib\/bbad095","article-title":"DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences","volume":"24","author":"Yao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B37","doi-asserted-by":"crossref","first-page":"bbac462","DOI":"10.1093\/bib\/bbac462","article-title":"ACP_MS: prediction of anticancer peptides based on feature extraction","volume":"23","author":"Zhou","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024032808412310500_btae142-B38","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/TCBB.2022.3223038","article-title":"PreVFs-RG: a deep hybrid model for identifying virulence factors based on residual block and gated recurrent unit","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024032808412310500_btae142-B39","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TCBB.2021.3126623","article-title":"Prediction of transcription factor binding sites with an attention augmented convolutional neural network","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae142\/56904593\/btae142.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae142\/57105164\/btae142.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/3\/btae142\/57105164\/btae142.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T22:27:26Z","timestamp":1731536846000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae142\/7624179"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae142","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,3,1]]},"article-number":"btae142"}}