{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T02:34:07Z","timestamp":1785033247912,"version":"3.55.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2024R321"],"award-info":[{"award-number":["PNURSP2024R321"]}]},{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2024R321"],"award-info":[{"award-number":["PNURSP2024R321"]}]},{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2024R321"],"award-info":[{"award-number":["PNURSP2024R321"]}]},{"name":"the European University of Atlantic"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Cancer"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Leukemia is a type of blood cell cancer that is in the bone marrow\u2019s blood-forming cells. Two types of Leukemia are acute and chronic; acute enhances fast and chronic growth gradually which are further classified into lymphocytic and myeloid leukemias. This work evaluates a unique deep convolutional neural network (CNN) classifier that improves identification precision by carefully examining concatenated peptide patterns. The study uses leukemia protein expression for experiments supporting two different techniques including independence and applied cross-validation. In addition to CNN, multilayer perceptron (MLP), gated recurrent unit (GRU), and recurrent neural network (RNN) are applied. The experimental results show that the CNN model surpasses competitors with its outstanding predictability in independent and cross-validation testing applied on different features extracted from protein expressions such as amino acid composition (AAC) with a group of AAC (GAAC), tripeptide composition (TPC) with a group of TPC (GTPC), and dipeptide composition (DPC) for calculating its accuracies with their receiver operating characteristic (ROC) curve. In independence testing, a feature expression of AAC and a group of GAAC are applied using MLP and CNN modules, and ROC curves are achieved with overall 100% accuracy for the detection of protein patterns. In cross-validation testing, a feature expression on a group of AAC and GAAC patterns achieved 98.33% accuracy which is the highest for the CNN module. Furthermore, ROC curves show a 0.965% extraordinary result for the GRU module. The findings show that the CNN model is excellent at figuring out leukemia illnesses from protein expressions with higher accuracy.<\/jats:p>","DOI":"10.1186\/s12885-024-12609-8","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T21:03:34Z","timestamp":1722027814000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Prediction of leukemia peptides using convolutional neural network and protein compositions"],"prefix":"10.1186","volume":"24","author":[{"given":"Seher Ansar","family":"Khawaja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Shoaib","family":"Farooq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kashif","family":"Ishaq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najah","family":"Alsubaie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanen","family":"Karamti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth Caro","family":"Montero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo Silva","family":"Alvarado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"12609_CR1","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s11899-012-0122-5","volume":"7","author":"I Iacobucci","year":"2012","unstructured":"Iacobucci I, Papayannidis C, Lonetti A, Ferrari A, Baccarani M, Martinelli G. Cytogenetic and molecular predictors of outcome in acute lymphocytic leukemia: recent developments. Curr Hematol Malignancy Rep. 2012;7:133\u201343.","journal-title":"Curr Hematol Malignancy Rep."},{"issue":"9","key":"12609_CR2","doi-asserted-by":"publisher","first-page":"3907","DOI":"10.7314\/APJCP.2014.15.9.3907","volume":"15","author":"MS Shaikh","year":"2014","unstructured":"Shaikh MS, Ali SS, Khurshid M, Fadoo Z. Chromosomal abnormalities in Pakistani children with acute lymphoblastic leukemia. Asian Pac J Cancer Prev. 2014;15(9):3907\u20139.","journal-title":"Asian Pac J Cancer Prev."},{"issue":"6","key":"12609_CR3","doi-asserted-by":"publisher","first-page":"3147","DOI":"10.21873\/anticanres.14296","volume":"40","author":"EK Amankwah","year":"2020","unstructured":"Amankwah EK, Devidas M, Teachey DT, Rabin KR, Brown PA. Six candidate miRNAs associated with early relapse in pediatric B-cell acute lymphoblastic leukemia. Anticancer Res. 2020;40(6):3147\u201353.","journal-title":"Anticancer Res."},{"issue":"4","key":"12609_CR4","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1016\/j.hoc.2009.04.009","volume":"23","author":"M Onciu","year":"2009","unstructured":"Onciu M. Acute lymphoblastic leukemia. Hematol \/ Oncol Clin N Am. 2009;23(4):655\u201374.","journal-title":"Hematol \/ Oncol Clin N Am."},{"issue":"1","key":"12609_CR5","doi-asserted-by":"publisher","first-page":"e2250219","DOI":"10.1001\/jamanetworkopen.2022.50219","volume":"6","author":"JM Geris","year":"2023","unstructured":"Geris JM, Schleiss MR, Hooten AJ, Langer E, Hernandez-Alvarado N, Roesler MA, et al. Evaluation of the association between congenital cytomegalovirus infection and pediatric acute lymphoblastic leukemia. JAMA Netw Open. 2023;6(1):e2250219\u2013e2250219.","journal-title":"JAMA Netw Open."},{"issue":"3","key":"12609_CR6","first-page":"150","volume":"59","author":"N Yasmeen","year":"2009","unstructured":"Yasmeen N, Ashraf S. sChildhood acute lymphoblastic leukaemia; epidemiology and clinicopathological features. JPMA J Pak Med Assoc. 2009;59(3):150\u20133.","journal-title":"JPMA J Pak Med Assoc."},{"issue":"6","key":"12609_CR7","first-page":"1165","volume":"120","author":"CH Pui","year":"2012","unstructured":"Pui CH, Mullighan CG, Evans WE, Relling MV. Pediatric acute lymphoblastic leukemia: where are we going and how do we get there? Blood J Am Soc Hematol. 2012;120(6):1165\u201374.","journal-title":"Blood J Am Soc Hematol."},{"key":"12609_CR8","doi-asserted-by":"publisher","first-page":"773202","DOI":"10.3389\/fgene.2021.773202","volume":"12","author":"D Zhao","year":"2021","unstructured":"Zhao D, Teng Z, Li Y, Chen D. iAIPs: identifying anti-inflammatory peptides using random forest. Front Genet. 2021;12:773202.","journal-title":"Front Genet."},{"issue":"4","key":"12609_CR9","doi-asserted-by":"publisher","first-page":"e0284522","DOI":"10.1371\/journal.pone.0284522","volume":"18","author":"A Raza","year":"2023","unstructured":"Raza A, Rustam F, Siddiqui HUR, de\u00a0la\u00a0Torre\u00a0D\u00edez I, Ashraf I. Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier. PLoS ONE. 2023;18(4):e0284522.","journal-title":"PLoS ONE."},{"issue":"3","key":"12609_CR10","doi-asserted-by":"publisher","first-page":"347","DOI":"10.3390\/healthcare11030347","volume":"11","author":"A Fatima","year":"2023","unstructured":"Fatima A, Shafi I, Afzal H, Mahmood K, de la Torre D\u00edez I, Lipari V, et al. Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection. Healthcare. 2023;11(3):347.","journal-title":"Healthcare."},{"key":"12609_CR11","doi-asserted-by":"publisher","first-page":"105425","DOI":"10.1016\/j.compbiomed.2022.105425","volume":"145","author":"I Shafi","year":"2022","unstructured":"Shafi I, Aziz A, Din S, Ashraf I. Reduced features set neural network approach based on high-resolution time-frequency images for cardiac abnormality detection. Comput Biol Med. 2022;145:105425.","journal-title":"Comput Biol Med."},{"issue":"7","key":"12609_CR12","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1093\/jamia\/ocaa060","volume":"27","author":"CE Coombes","year":"2020","unstructured":"Coombes CE, Abrams ZB, Li S, Abruzzo LV, Coombes KR. Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. J Am Med Inform Assoc. 2020;27(7):1019\u201327.","journal-title":"J Am Med Inform Assoc."},{"issue":"3","key":"12609_CR13","doi-asserted-by":"publisher","first-page":"382","DOI":"10.25122\/jml-2019-0090","volume":"13","author":"E Nazari","year":"2020","unstructured":"Nazari E, Farzin AH, Aghemiri M, Avan A, Tara M, Tabesh H. Deep learning for acute myeloid leukemia diagnosis. J Med Life. 2020;13(3):382.","journal-title":"J Med Life."},{"issue":"1","key":"12609_CR14","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/s12859-022-04807-7","volume":"23","author":"B Hanczar","year":"2022","unstructured":"Hanczar B, Bourgeais V, Zehraoui F. Assessment of deep learning and transfer learning for cancer prediction based on gene expression data. BMC Bioinforma. 2022;23(1):262.","journal-title":"BMC Bioinforma."},{"key":"12609_CR15","doi-asserted-by":"crossref","unstructured":"Ratley A, Minj J, Patre P. Leukemia disease detection and classification using machine learning approaches: a review. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T).\u00a0Chhattisgarh: IEEE; 2020. p. 161\u20135.","DOI":"10.1109\/ICPC2T48082.2020.9071471"},{"key":"12609_CR16","doi-asserted-by":"publisher","first-page":"496","DOI":"10.47750\/pnr.2022.13.S09.056","volume":"13","author":"K Sridhar","year":"2022","unstructured":"Sridhar K, Yeruva AR, Renjith P, Dixit A, Jamshed A, Rastogi R. Enhanced Machine learning algorithms Lightweight Ensemble Classification of Normal versus Leukemic Cel. J Pharm Negat Results. 2022;13:496\u2013505.","journal-title":"J Pharm Negat Results."},{"issue":"1","key":"12609_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04432-w","volume":"22","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Archer KJ. Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia. BMC Bioinformatics. 2021;22(1):1\u201317.","journal-title":"BMC Bioinformatics."},{"issue":"21","key":"12609_CR18","doi-asserted-by":"publisher","first-page":"10760","DOI":"10.3390\/app122110760","volume":"12","author":"NM Sallam","year":"2022","unstructured":"Sallam NM, Saleh AI, Arafat Ali H, Abdelsalam MM. An efficient strategy for blood diseases detection based on grey wolf optimization as feature selection and machine learning techniques. Appl Sci. 2022;12(21):10760.","journal-title":"Appl Sci."},{"issue":"1","key":"12609_CR19","doi-asserted-by":"publisher","first-page":"13594","DOI":"10.1038\/s41598-021-93124-9","volume":"11","author":"KY Huang","year":"2021","unstructured":"Huang KY, Tseng YJ, Kao HJ, Chen CH, Yang HH, Weng SL. Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties. Sci Rep. 2021;11(1):13594.","journal-title":"Sci Rep."},{"issue":"23","key":"12609_CR20","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150\u20132.","journal-title":"Bioinformatics."},{"issue":"9","key":"12609_CR21","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1038\/nbt.4233","volume":"36","author":"M Wainberg","year":"2018","unstructured":"Wainberg M, Merico D, Delong A, Frey BJ. Deep learning in biomedicine. Nat Biotechnol. 2018;36(9):829\u201338.","journal-title":"Nat Biotechnol."},{"issue":"14","key":"12609_CR22","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","volume":"34","author":"Z Chen","year":"2018","unstructured":"Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, et al. iFeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics. 2018;34(14):2499\u2013502.","journal-title":"Bioinformatics."},{"key":"12609_CR23","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.chemolab.2018.09.007","volume":"182","author":"M Kabir","year":"2018","unstructured":"Kabir M, Arif M, Ahmad S, Ali Z, Swati ZNK, Yu DJ. Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information. Chemometr Intell Lab Syst. 2018;182:158\u201365.","journal-title":"Chemometr Intell Lab Syst."},{"issue":"8","key":"12609_CR24","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.tips.2019.06.004","volume":"40","author":"HS Chan","year":"2019","unstructured":"Chan HS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci. 2019;40(8):592\u2013604.","journal-title":"Trends Pharmacol Sci."},{"issue":"12","key":"12609_CR25","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1038\/nrd2961","volume":"8","author":"B Munos","year":"2009","unstructured":"Munos B. Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009;8(12):959\u201368.","journal-title":"Nat Rev Drug Discov."},{"issue":"3","key":"12609_CR26","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.3390\/ijms24032217","volume":"24","author":"J Zhao","year":"2023","unstructured":"Zhao J, Yan W, Yang Y. DeepTP: A Deep Learning Model for Thermophilic Protein Prediction. Int J Mol Sci. 2023;24(3):2217.","journal-title":"Int J Mol Sci."},{"key":"12609_CR27","doi-asserted-by":"publisher","first-page":"109535","DOI":"10.1109\/ACCESS.2020.2999394","volume":"8","author":"JN Sun","year":"2020","unstructured":"Sun JN, Yang HY, Yao J, Ding H, Han SG, Wu CY, et al. Prediction of cyclin protein using two-step feature selection technique. IEEE Access. 2020;8:109535\u201342.","journal-title":"IEEE Access."},{"issue":"7","key":"12609_CR28","doi-asserted-by":"publisher","first-page":"e22930","DOI":"10.1371\/journal.pone.0022930","volume":"6","author":"Z Chen","year":"2011","unstructured":"Chen Z, Chen YZ, Wang XF, Wang C, Yan RX, Zhang Z. Prediction of ubiquitination sites by using the composition of k-spaced amino acid pairs. PLoS ONE. 2011;6(7):e22930.","journal-title":"PLoS ONE."},{"key":"12609_CR29","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.jare.2019.03.008","volume":"18","author":"M Juez-Gil","year":"2019","unstructured":"Juez-Gil M, Erdakov IN, Bustillo A, Pimenov DY. A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes. J Adv Res. 2019;18:173\u201384.","journal-title":"J Adv Res."},{"issue":"7\u20138","key":"12609_CR30","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.drudis.2007.02.015","volume":"12","author":"LY Han","year":"2007","unstructured":"Han LY, Zheng CJ, Xie B, Jia J, Ma XH, Zhu F, et al. Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discov Today. 2007;12(7\u20138):304\u201313.","journal-title":"Drug Discov Today."},{"key":"12609_CR31","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.jare.2022.01.009","volume":"41","author":"L Yu","year":"2022","unstructured":"Yu L, Xue L, Liu F, Li Y, Jing R, Luo J. The applications of deep learning algorithms on in silico druggable proteins identification. J Adv Res. 2022;41:219\u201331.","journal-title":"J Adv Res."},{"key":"12609_CR32","doi-asserted-by":"crossref","unstructured":"Wassan JT, Zheng H, et\u00a0al. Measurements of Accuracy in Biostatistics. In: Encyclopedia of Bioinformatics and Computational Biology.\u00a0Salem:\u00a0Research Trentz Academy Publishing Education Services; 2018. p. 685\u201390.","DOI":"10.1016\/B978-0-12-809633-8.20355-5"},{"issue":"5","key":"12609_CR33","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1039\/D0MO00025F","volume":"16","author":"M Chaudhari","year":"2020","unstructured":"Chaudhari M, Thapa N, Roy K, Newman RH, Saigo H, Dukka B. DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins. Mol Omics. 2020;16(5):448\u201354.","journal-title":"Mol Omics."},{"issue":"5","key":"12609_CR34","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1109\/TCBB.2020.2968441","volume":"18","author":"MA Akmal","year":"2021","unstructured":"Akmal MA, Hussain W, Rasool N, Khan YD, Khan SA, Chou KC. Using CHOU\u2019S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE\/ACM Trans Comput Biol Bioinforma. 2021;18(5):2045\u201356.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma."}],"updated-by":[{"DOI":"10.1186\/s12885-024-12756-y","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000}}],"container-title":["BMC Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12885-024-12609-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12885-024-12609-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12885-024-12609-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T06:05:04Z","timestamp":1723183504000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmccancer.biomedcentral.com\/articles\/10.1186\/s12885-024-12609-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["12609"],"URL":"https:\/\/doi.org\/10.1186\/s12885-024-12609-8","relation":{"correction":[{"id-type":"doi","id":"10.1186\/s12885-024-12756-y","asserted-by":"object"}]},"ISSN":["1471-2407"],"issn-type":[{"value":"1471-2407","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]},"assertion":[{"value":"26 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s12885-024-12756-y","URL":"https:\/\/doi.org\/10.1186\/s12885-024-12756-y","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"900"}}