{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:54:12Z","timestamp":1774659252253,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500291","type":"print"},{"value":"9789819500307","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0030-7_19","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T07:35:25Z","timestamp":1753342525000},"page":"221-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CALM-AcPEP: Predicting Anticancer Peptides Using Cross-Attention and Pre-Trained Language Model"],"prefix":"10.1007","author":[{"given":"Xinke","family":"Zhan","sequence":"first","affiliation":[]},{"given":"Tiantao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Pratiti","family":"Bhadra","sequence":"additional","affiliation":[]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhuhong","family":"You","sequence":"additional","affiliation":[]},{"given":"Shirley W. I.","family":"Siu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"issue":"3","key":"19_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209\u2013249 (2021)","journal-title":"CA Cancer J. Clin."},{"issue":"11","key":"19_CR2","doi-asserted-by":"publisher","first-page":"e442","DOI":"10.1371\/journal.pmed.0030442","volume":"3","author":"CD Mathers","year":"2006","unstructured":"Mathers, C.D., Loncar, D.: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006)","journal-title":"PLoS Med."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jconrel.2018.11.004","volume":"292","author":"F Araste","year":"2018","unstructured":"Araste, F., Abnous, K., Hashemi, M., et al.: Peptide-based targeted therapeutics: focus on cancer treatment. J. Control. Release 292, 141\u2013162 (2018)","journal-title":"J. Control. Release"},{"issue":"14","key":"19_CR4","doi-asserted-by":"publisher","first-page":"16820","DOI":"10.1021\/acsomega.4c01374","volume":"9","author":"M Xu","year":"2024","unstructured":"Xu, M., Pang, J., Ye, Y., et al.: Integrating traditional machine learning and deep learning for precision screening of anticancer peptides: a novel approach for efficient drug discovery. ACS Omega 9(14), 16820\u201316831 (2024)","journal-title":"ACS Omega"},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"108063","DOI":"10.1016\/j.compbiomed.2024.108063","volume":"170","author":"J Bian","year":"2024","unstructured":"Bian, J., Liu, X., Dong, G., et al.: ACP-ML: a sequence-based method for anticancer peptide prediction. Comput. Biol. Med. 170, 108063 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"19_CR6","doi-asserted-by":"publisher","first-page":"3789","DOI":"10.1021\/acs.jcim.1c00181","volume":"61","author":"J Chen","year":"2021","unstructured":"Chen, J., Cheong, H.H., Siu, S.W.: xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. J. Chem. Inf. Model. 61(8), 3789\u20133803 (2021)","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"19_CR7","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.1038\/srep02984","volume":"3","author":"A Tyagi","year":"2013","unstructured":"Tyagi, A., Kapoor, P., Kumar, R., et al.: In silico models for designing and discovering novel anticancer peptides. Sci. Rep. 3(1), 2984 (2013)","journal-title":"Sci. Rep."},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Agrawal, P., Bhagat, D., Mahalwal, M., et al.: AntiCP 2.0: an updated model for predicting anticancer peptides. Brief. Bioinform. 22(3), bbaa153 (2021)","DOI":"10.1093\/bib\/bbaa153"},{"key":"19_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.omtn.2019.04.025","volume":"17","author":"HC Yi","year":"2019","unstructured":"Yi, H.C., You, Z.H., Zhou, X., et al.: ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation. Mol. Ther. Nucleic acids 17, 1\u20139 (2019)","journal-title":"Mol. Ther. Nucleic acids"},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"108538","DOI":"10.1016\/j.compbiomed.2024.108538","volume":"176","author":"H Ghafoor","year":"2024","unstructured":"Ghafoor, H., Asim, M.N., Ibrahim, M.A., et al.: Capture: comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder. Comput. Biol. Med. 176, 108538 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"17","key":"19_CR11","doi-asserted-by":"publisher","first-page":"168687","DOI":"10.1016\/j.jmb.2024.168687","volume":"436","author":"VK Sangaraju","year":"2024","unstructured":"Sangaraju, V.K., Pham, N.T., Wei, L., Xue, Yu., Manavalan, B.: MACPpred 2.0: stacked deep learning for anticancer peptide prediction with integrated spatial and probabilistic feature representations. J. Mol. Biol. 436(17), 168687 (2024). https:\/\/doi.org\/10.1016\/j.jmb.2024.168687","journal-title":"J. Mol. Biol."},{"issue":"1","key":"19_CR12","doi-asserted-by":"publisher","first-page":"23676","DOI":"10.1038\/s41598-021-02703-3","volume":"11","author":"S Ahmed","year":"2021","unstructured":"Ahmed, S., Muhammod, R., Khan, Z.H., et al.: ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides. Sci. Rep. 11(1), 23676 (2021)","journal-title":"Sci. Rep."},{"issue":"1","key":"19_CR13","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1186\/s12859-023-05475-x","volume":"24","author":"J Sui","year":"2023","unstructured":"Sui, J., Chen, J., Chen, Y., et al.: Identification of plant vacuole proteins by using graph neural network and contact maps. BMC Bioinform. 24(1), 357 (2023)","journal-title":"BMC Bioinform."},{"issue":"15","key":"19_CR14","doi-asserted-by":"publisher","first-page":"6174","DOI":"10.1021\/acs.jcim.4c00501","volume":"64","author":"HH Cheong","year":"2024","unstructured":"Cheong, H.H., Zuo, W., Chen, J., et al.: Identification of anticancer peptides from the genome of Candida albicans: in silico screening, in vitro and in vivo validations. J. Chem. Inf. Model. 64(15), 6174\u20136189 (2024)","journal-title":"J. Chem. Inf. Model."},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Cai, J., Yan, J., Un, C., et al.: BERT-AmPEP60: a BERT-based transfer learning approach to predict the minimum inhibitory concentrations of antimicrobial peptides for Escherichia coli and staphylococcus aureus. J. Chem. Inf. Model. (2025)","DOI":"10.1021\/acs.jcim.4c01749"},{"issue":"24","key":"19_CR16","doi-asserted-by":"publisher","first-page":"4684","DOI":"10.1093\/bioinformatics\/btab560","volume":"37","author":"W He","year":"2021","unstructured":"He, W., Wang, Y., Cui, L., et al.: Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides. Bioinformatics 37(24), 4684\u20134693 (2021)","journal-title":"Bioinformatics"},{"issue":"5","key":"19_CR17","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1093\/bioinformatics\/btq003","volume":"26","author":"Y Huang","year":"2010","unstructured":"Huang, Y., Niu, B., Gao, Y., et al.: CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26(5), 680\u2013682 (2010)","journal-title":"Bioinformatics"},{"issue":"6637","key":"19_CR18","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin, Z., Akin, H., Rao, R., et al.: Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123\u20131130 (2023)","journal-title":"Science"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Pan, X., Ge, C., Lu, R., et al.: On the integration of self-attention and convolution. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 815\u2013825 (2022)","DOI":"10.1109\/CVPR52688.2022.00089"},{"issue":"5","key":"19_CR20","doi-asserted-by":"publisher","first-page":"1846","DOI":"10.1093\/bib\/bbz088","volume":"21","author":"B Rao","year":"2020","unstructured":"Rao, B., Zhou, C., Zhang, G., et al.: ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides. Brief. Bioinform. 21(5), 1846\u20131855 (2020)","journal-title":"Brief. Bioinform."},{"key":"19_CR21","doi-asserted-by":"publisher","first-page":"106844","DOI":"10.1016\/j.compbiomed.2023.106844","volume":"158","author":"H Deng","year":"2023","unstructured":"Deng, H., Ding, M., Wang, Y., et al.: ACP-MLC: a two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types. Comput. Biol. Med. 158, 106844 (2023)","journal-title":"Comput. Biol. Med."},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Lv, Z., Cui, F., Zou, Q., et al.: Anticancer peptides prediction with deep representation learning features. Brief. Bioinform. 22(5), bbab008 (2021)","DOI":"10.1093\/bib\/bbab008"},{"key":"19_CR23","doi-asserted-by":"publisher","first-page":"105868","DOI":"10.1016\/j.compbiomed.2022.105868","volume":"148","author":"L Zhu","year":"2022","unstructured":"Zhu, L., Ye, C., Hu, X., et al.: ACP-check: an anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy. Comput. Biol. Med. 148, 105868 (2022)","journal-title":"Comput. Biol. Med."},{"key":"19_CR24","doi-asserted-by":"publisher","first-page":"105459","DOI":"10.1016\/j.compbiomed.2022.105459","volume":"145","author":"G Feng","year":"2022","unstructured":"Feng, G., Yao, H., Li, C., et al.: ME-ACP: multi-view neural networks with ensemble model for identification of anticancer peptides. Comput. Biol. Med. 145, 105459 (2022)","journal-title":"Comput. Biol. Med."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0030-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:09:38Z","timestamp":1774656578000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0030-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500291","9789819500307"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0030-7_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}