{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:09:48Z","timestamp":1780765788486,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"College of Arts, Media and Technology, Chiang Mai University"},{"name":"Specific League Funds from Mahidol University"},{"name":"National Research Council of Thailand and Mahidol University","award":["N42A660380"],"award-info":[{"award-number":["N42A660380"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/2pmlab.camt.cmu.ac.th\/StackTTCA\">http:\/\/2pmlab.camt.cmu.ac.th\/StackTTCA<\/jats:ext-link>) to maximize user convenience for high-throughput screening of novel TTCAs.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05421-x","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T13:01:58Z","timestamp":1690549318000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens"],"prefix":"10.1186","volume":"24","author":[{"given":"Phasit","family":"Charoenkwan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nalini","family":"Schaduangrat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"issue":"11","key":"5421_CR1","doi-asserted-by":"publisher","first-page":"5117","DOI":"10.4049\/jimmunol.1501657","volume":"195","author":"S Ilyas","year":"2015","unstructured":"Ilyas S, Yang JC. Landscape of tumor antigens in T cell immunotherapy. J Immunol. 2015;195(11):5117\u201322.","journal-title":"J Immunol"},{"issue":"2","key":"5421_CR2","doi-asserted-by":"publisher","first-page":"392","DOI":"10.4049\/jimmunol.1701413","volume":"200","author":"AE Zamora","year":"2018","unstructured":"Zamora AE, Crawford JC, Thomas PG. Hitting the target: how T cells detect and eliminate tumors. J Immunol. 2018;200(2):392\u20139.","journal-title":"J Immunol"},{"issue":"25","key":"5421_CR3","doi-asserted-by":"publisher","first-page":"7807","DOI":"10.7150\/thno.37194","volume":"9","author":"L Zhang","year":"2019","unstructured":"Zhang L, Huang Y, Lindstrom AR, Lin T-Y, Lam KS, Li Y. Peptide-based materials for cancer immunotherapy. Theranostics. 2019;9(25):7807.","journal-title":"Theranostics"},{"key":"5421_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fimmu.2019.00008","volume":"10","author":"K Vermaelen","year":"2019","unstructured":"Vermaelen K. Vaccine strategies to improve anti-cancer cellular immune responses. Front Immunol. 2019;10:8.","journal-title":"Front Immunol"},{"issue":"7780","key":"5421_CR5","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41586-019-1671-8","volume":"574","author":"E Alspach","year":"2019","unstructured":"Alspach E, et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature. 2019;574(7780):696\u2013701.","journal-title":"Nature"},{"issue":"4","key":"5421_CR6","doi-asserted-by":"publisher","first-page":"328","DOI":"10.2174\/187153009789839156","volume":"9","author":"K Breckpot","year":"2009","unstructured":"Breckpot K, Escors D. Dendritic cells for active anti-cancer immunotherapy: targeting activation pathways through genetic modification. Endocr Metab Immune Disord Drug Targets (Former Curr Drug Targets Immune Endocr Metab Disord). 2009;9(4):328\u201343.","journal-title":"Endocr Metab Immune Disord Drug Targets (Former Curr Drug Targets Immune Endocr Metab Disord)"},{"issue":"1","key":"5421_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.2174\/1389201019666180418095526","volume":"19","author":"AN Miliotou","year":"2018","unstructured":"Miliotou AN, Papadopoulou LC. CAR T-cell therapy: a new era in cancer immunotherapy. Curr Pharm Biotechnol. 2018;19(1):5\u201318.","journal-title":"Curr Pharm Biotechnol"},{"issue":"10","key":"5421_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003266","volume":"9","author":"JJ Calis","year":"2013","unstructured":"Calis JJ, et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol. 2013;9(10): e1003266.","journal-title":"PLoS Comput Biol"},{"issue":"14","key":"5421_CR9","doi-asserted-by":"publisher","first-page":"E1754","DOI":"10.1073\/pnas.1500973112","volume":"112","author":"D Chowell","year":"2015","unstructured":"Chowell D, et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc Natl Acad Sci. 2015;112(14):E1754\u201362.","journal-title":"Proc Natl Acad Sci"},{"issue":"5","key":"5421_CR10","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1111\/cas.12650","volume":"106","author":"Y Nishimura","year":"2015","unstructured":"Nishimura Y, Tomita Y, Yuno A, Yoshitake Y, Shinohara M. Cancer immunotherapy using novel tumor-associated antigenic peptides identified by genome-wide cDNA microarray analyses. Cancer Sci. 2015;106(5):505\u201311.","journal-title":"Cancer Sci"},{"issue":"D1","key":"5421_CR11","doi-asserted-by":"publisher","first-page":"D339","DOI":"10.1093\/nar\/gky1006","volume":"47","author":"R Vita","year":"2019","unstructured":"Vita R, et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res. 2019;47(D1):D339\u201343.","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"5421_CR12","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s00262-017-1978-y","volume":"66","author":"LR Olsen","year":"2017","unstructured":"Olsen LR, Tongchusak S, Lin H, Reinherz EL, Brusic V, Zhang GL. TANTIGEN: a comprehensive database of tumor T cell antigens. Cancer Immunol Immunother. 2017;66(6):731\u20135.","journal-title":"Cancer Immunol Immunother"},{"issue":"8","key":"5421_CR13","first-page":"1","volume":"22","author":"G Zhang","year":"2021","unstructured":"Zhang G, Chitkushev L, Olsen LR, Keskin DB, Brusic V. TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes. BMC Bioinform. 2021;22(8):1\u20138.","journal-title":"BMC Bioinform"},{"issue":"23","key":"5421_CR14","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.1093\/bioinformatics\/bty451","volume":"34","author":"L Wei","year":"2018","unstructured":"Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics. 2018;34(23):4007\u201316.","journal-title":"Bioinformatics"},{"issue":"5","key":"5421_CR15","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, Su R, Wei L. ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides. Brief Bioinform. 2020;21(5):1846\u201355.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5421_CR16","first-page":"11","volume":"21","author":"X Qiang","year":"2020","unstructured":"Qiang X, Zhou C, Ye X, Du P-F, Su R, Wei L. CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning. Brief Bioinform. 2020;21(1):11\u201323.","journal-title":"Brief Bioinform"},{"key":"5421_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2019.107103","volume":"83","author":"JFB Lissabet","year":"2019","unstructured":"Lissabet JFB, Bel\u00e9n LH, Farias JG. TTAgP 1.0: a computational tool for the specific prediction of tumor T cell antigens. Comput Biol Chem. 2019;83: 107103.","journal-title":"Comput Biol Chem"},{"key":"5421_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ab.2020.113747","volume":"599","author":"P Charoenkwan","year":"2020","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Shoombuatong W. iTTCA-Hybrid: improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation. Anal Biochem. 2020;599: 113747.","journal-title":"Anal Biochem"},{"key":"5421_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107452","volume":"91","author":"J Herrera-Bravo","year":"2021","unstructured":"Herrera-Bravo J, Bel\u00e9n LH, Farias JG, Beltr\u0644n JF. TAP 1.0: a robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties. Comput Biol Chem. 2021;91: 107452.","journal-title":"Comput Biol Chem"},{"issue":"1","key":"5421_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-021-03084-x","volume":"19","author":"S Jiao","year":"2021","unstructured":"Jiao S, Zou Q, Guo H, Shi L. iTTCA-RF: a random forest predictor for tumor T cell antigens. J Transl Med. 2021;19(1):1\u201311.","journal-title":"J Transl Med"},{"issue":"5","key":"5421_CR21","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s00251-022-01258-5","volume":"74","author":"H Zou","year":"2022","unstructured":"Zou H, Yang F, Yin Z. iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion. Immunogenetics. 2022;74(5):447\u201354.","journal-title":"Immunogenetics"},{"key":"5421_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106368","volume":"152","author":"P Charoenkwan","year":"2023","unstructured":"Charoenkwan P, Pipattanaboon C, Nantasenamat C, Hasan MM, Moni MA, Shoombuatong W. PSRTTCA: a new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning. Comput Biol Med. 2023;152: 106368.","journal-title":"Comput Biol Med"},{"issue":"5","key":"5421_CR23","doi-asserted-by":"publisher","first-page":"bbac335","DOI":"10.1093\/bib\/bbac335","volume":"23","author":"T Zhang","year":"2022","unstructured":"Zhang T, Jia Y, Li H, Xu D, Zhou J, Wang G. CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins. Brief Bioinform. 2022;23(5):bbac335.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5421_CR24","doi-asserted-by":"publisher","first-page":"bbab396","DOI":"10.1093\/bib\/bbab396","volume":"23","author":"H Wu","year":"2022","unstructured":"Wu H, et al. scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding. Brief Bioinform. 2022;23(1):bbab396.","journal-title":"Brief Bioinform"},{"key":"5421_CR25","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"5421_CR26","doi-asserted-by":"publisher","first-page":"4106","DOI":"10.1038\/s41598-022-08173-5","volume":"12","author":"S Ahmad","year":"2022","unstructured":"Ahmad S, et al. SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. Sci Rep. 2022;12(1):4106.","journal-title":"Sci Rep"},{"key":"5421_CR27","doi-asserted-by":"publisher","first-page":"105704","DOI":"10.1016\/j.compbiomed.2022.105704","volume":"146","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Moni MA, Manavalan B, Shoombuatong W. SAPPHIRE: a stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. Comput Biol Med. 2022;146:105704.","journal-title":"Comput Biol Med"},{"key":"5421_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105700","volume":"148","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Moni MA, Manavalan B, Shoombuatong W. NEPTUNE: a novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput Biol Med. 2022;148: 105700.","journal-title":"Comput Biol Med"},{"key":"5421_CR29","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jbi.2017.09.011","volume":"75","author":"C Xu","year":"2017","unstructured":"Xu C, Ge L, Zhang Y, Dehmer M, Gutman I. Computational prediction of therapeutic peptides based on graph index. J Biomed Inform. 2017;75:63\u20139.","journal-title":"J Biomed Inform"},{"issue":"1","key":"5421_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-11897-z","volume":"12","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, et al. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep. 2022;12(1):1\u201314.","journal-title":"Sci Rep"},{"issue":"9","key":"5421_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.104883","volume":"25","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Moni MA, Shoombuatong W, Manavalan B. Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. Iscience. 2022;25(9): 104883.","journal-title":"Iscience"},{"issue":"1","key":"5421_CR32","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3390\/ijms21010075","volume":"21","author":"P Charoenkwan","year":"2019","unstructured":"Charoenkwan P, Schaduangrat N, Nantasenamat C, Piacham T, Shoombuatong W. iQSP: a sequence-based tool for the prediction and analysis of quorum sensing peptides using informative physicochemical properties. Int J Mol Sci. 2019;21(1):75.","journal-title":"Int J Mol Sci"},{"issue":"23","key":"5421_CR33","doi-asserted-by":"publisher","first-page":"13124","DOI":"10.3390\/ijms222313124","volume":"22","author":"P Charoenkwan","year":"2021","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Manavalan B, Shoombuatong W. UMPred-FRL: a new approach for accurate prediction of umami peptides using feature representation learning. Int J Mol Sci. 2021;22(23):13124.","journal-title":"Int J Mol Sci"},{"key":"5421_CR34","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ymeth.2021.12.001","volume":"204","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Manavalan B, Shoombuatong W. StackDPPIV: a novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Methods. 2022;204:189\u201398.","journal-title":"Methods"},{"key":"5421_CR35","doi-asserted-by":"publisher","first-page":"105700","DOI":"10.1016\/j.compbiomed.2022.105700","volume":"148","author":"P Charoenkwan","year":"2022","unstructured":"Charoenkwan P, Schaduangrat N, Lio P, Moni MA, Manavalan B, Shoombuatong W. NEPTUNE: a novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput Biol Med. 2022;148:105700.","journal-title":"Comput Biol Med"},{"issue":"3","key":"5421_CR36","doi-asserted-by":"publisher","first-page":"EL140","DOI":"10.1121\/1.4865840","volume":"135","author":"M Azadpour","year":"2014","unstructured":"Azadpour M, McKay CM, Smith RL. Estimating confidence intervals for information transfer analysis of confusion matrices. J Acoust Soc Am. 2014;135(3):EL140\u20136.","journal-title":"J Acoust Soc Am"},{"key":"5421_CR37","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.omtn.2019.05.028","volume":"17","author":"H-Y Lai","year":"2019","unstructured":"Lai H-Y, et al. iProEP: a computational predictor for predicting promoter. Mol Ther Nucl Acids. 2019;17:337\u201346.","journal-title":"Mol Ther Nucl Acids"},{"issue":"4","key":"5421_CR38","doi-asserted-by":"publisher","first-page":"bbaa255","DOI":"10.1093\/bib\/bbaa255","volume":"22","author":"H Lv","year":"2021","unstructured":"Lv H, Dao F-Y, Guan Z-X, Yang H, Li Y-W, Lin H. Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method. Brief Bioinform. 2021;22(4):bbaa255.","journal-title":"Brief Bioinform"},{"key":"5421_CR39","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1093\/bib\/bbz048","volume":"21","author":"H Lv","year":"2019","unstructured":"Lv H, Zhang Z-M, Li S-H, Tan J-X, Chen W, Lin H. Evaluation of different computational methods on 5-methylcytosine sites identification. Brief Bioinform. 2019;21:982\u201395.","journal-title":"Brief Bioinform"},{"issue":"24","key":"5421_CR40","doi-asserted-by":"publisher","first-page":"4196","DOI":"10.1093\/bioinformatics\/bty508","volume":"34","author":"Z-D Su","year":"2018","unstructured":"Su Z-D, et al. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics. 2018;34(24):4196\u2013204.","journal-title":"Bioinformatics"},{"issue":"6","key":"5421_CR41","doi-asserted-by":"publisher","first-page":"bbab278","DOI":"10.1093\/bib\/bbab278","volume":"22","author":"M Ullah","year":"2021","unstructured":"Ullah M, Han K, Hadi F, Xu J, Song J, Yu D-J. PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection. Brief Bioinform. 2021;22(6):bbab278.","journal-title":"Brief Bioinform"},{"issue":"9","key":"5421_CR42","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","volume":"5","author":"JN Mandrekar","year":"2010","unstructured":"Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315\u20136.","journal-title":"J Thorac Oncol"},{"issue":"3","key":"5421_CR43","doi-asserted-by":"publisher","first-page":"bbaa125","DOI":"10.1093\/bib\/bbaa125","volume":"22","author":"R Xie","year":"2021","unstructured":"Xie R, et al. DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy. Brief Bioinform. 2021;22(3):bbaa125.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5421_CR44","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014;15(1):3221\u201345.","journal-title":"J Mach Learn Res"},{"issue":"11","key":"5421_CR45","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579\u2013605.","journal-title":"J Mach Learn Res"},{"issue":"2","key":"5421_CR46","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1093\/bib\/bby124","volume":"21","author":"R Su","year":"2020","unstructured":"Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform. 2020;21(2):408\u201320.","journal-title":"Brief Bioinform"},{"issue":"4","key":"5421_CR47","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1002\/med.21658","volume":"40","author":"S Basith","year":"2020","unstructured":"Basith S, Manavalan B, Hwan Shin T, Lee G. Machine intelligence in peptide therapeutics: a next-generation tool for rapid disease screening. Med Res Rev. 2020;40(4):1276\u2013314.","journal-title":"Med Res Rev"},{"issue":"6","key":"5421_CR48","doi-asserted-by":"publisher","first-page":"bbab244","DOI":"10.1093\/bib\/bbab244","volume":"22","author":"H Lv","year":"2021","unstructured":"Lv H, Dao F-Y, Zulfiqar H, Lin H. DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach. Brief Bioinform. 2021;22(6):bbab244.","journal-title":"Brief Bioinform"},{"issue":"17","key":"5421_CR49","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","volume":"37","author":"P Charoenkwan","year":"2021","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W. BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics. 2021;37(17):2556\u201362.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05421-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05421-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05421-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T13:02:44Z","timestamp":1690549364000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05421-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5421"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05421-x","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2023","order":3,"name":"first_online","label":"First Online","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":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"301"}}