{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:11:14Z","timestamp":1779174674470,"version":"3.51.4"},"reference-count":79,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University","award":["IMSIU-DDRSP2602"],"award-info":[{"award-number":["IMSIU-DDRSP2602"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-026-06440-0","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T11:35:14Z","timestamp":1775907314000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GGAR: gradient guided adaptive regularization enhances deep learning classification of brassica species using codon usage bias"],"prefix":"10.1186","volume":"27","author":[{"given":"Anjum","family":"Shahzad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheeraz","family":"Akram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tahir","family":"Mehmood","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"issue":"1","key":"6440_CR1","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/nrg2899","volume":"12","author":"JB Plotkin","year":"2011","unstructured":"Plotkin JB, Kudla G. Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet. 2011;12(1):32\u201342.","journal-title":"Nat Rev Genet"},{"issue":"1","key":"6440_CR2","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1146\/annurev.genet.42.110807.091442","volume":"42","author":"R Hershberg","year":"2008","unstructured":"Hershberg R, Petrov DA. Selection on codon bias. Annu Rev Genet. 2008;42(1):287\u201399.","journal-title":"Annu Rev Genet"},{"key":"6440_CR3","unstructured":"Touchon TH, Ghosh S. Determination of the relative importance of gene function or taxonomic grouping to codon usage bias using cluster analysis and SVMs. In: Proceedings of the 2006 IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB\u201906). 2006;4133191:335\u2013342."},{"issue":"4","key":"6440_CR4","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1007\/s11105-013-0691-z","volume":"32","author":"H Liu","year":"2014","unstructured":"Liu H, He J, Ding Z, Zhang X. Comparative analysis of codon usage patterns in chloroplast genomes of the Asteraceae family. Plant Mol Biol Rep. 2014;32(4):828\u201340.","journal-title":"Plant Mol Biology Report"},{"key":"6440_CR5","doi-asserted-by":"publisher","unstructured":"Hallee L, Khomtchouk BB. Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life. Sci Rep. 2023;13(1):2088. https:\/\/doi.org\/10.1038\/s41598-023-28965-7","DOI":"10.1038\/s41598-023-28965-7"},{"key":"6440_CR6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.margen.2016.10.001","volume":"32","author":"A Gupta","year":"2017","unstructured":"Gupta A, Singh JK. Genome-wide comparative analysis of codon usage bias and codon context patterns among cyanobacterial genomes. Mar Genom. 2017;32:31\u20139.","journal-title":"Mar Genom"},{"key":"6440_CR7","doi-asserted-by":"crossref","unstructured":"Khan MH, Rehman AU, Nisa S. Natural selection as the primary driver of codon usage bias in the mitochondrial genomes of three Medicago Species. Genes. 2025;16(6):673.","DOI":"10.3390\/genes16060673"},{"key":"6440_CR8","doi-asserted-by":"crossref","unstructured":"Wang X, Zhou Y, Li F. Comparative analysis of the codon usage pattern in the chloroplast genomes of Gnetales species. Int J Mol Sci. 2024;25(19):10622.","DOI":"10.3390\/ijms251910622"},{"key":"6440_CR9","doi-asserted-by":"crossref","unstructured":"Zhang T, Liu M, Chen R. Genomic factors shaping codon usage across the Saccharomycotina subphylum. G3 Genes Genom Genet. 2024;14(11):jkae207.","DOI":"10.1093\/g3journal\/jkae207"},{"key":"6440_CR10","doi-asserted-by":"publisher","unstructured":"Shahzad A, Mehmood T, Akram S. Gradient responsive regularization: a deep learning framework for codon frequency based classification of evolutionarily conserved genes. BMC Genom Data. 2025;26(1):72. https:\/\/doi.org\/10.1186\/s12863-025-01358-7","DOI":"10.1186\/s12863-025-01358-7"},{"key":"6440_CR11","doi-asserted-by":"publisher","unstructured":"Shahzad A, Arfan M, Khalid N. Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences. Sci Rep. 2025;15(1):33417. https:\/\/doi.org\/10.1038\/s41598-025-18814-0","DOI":"10.1038\/s41598-025-18814-0"},{"issue":"3","key":"6440_CR12","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1093\/genetics\/129.3.897","volume":"129","author":"M Bulmer","year":"1991","unstructured":"Bulmer M. The selection-mutation-drift theory of synonymous codon usage. Genetics. 1991;129(3):897\u2013907.","journal-title":"Genetics"},{"issue":"1544","key":"6440_CR13","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1098\/rstb.2009.0305","volume":"365","author":"PM Sharp","year":"2010","unstructured":"Sharp PM, Emery LR, Zeng K. Forces that influence the evolution of codon bias. Philosoph Trans R Soc B Biol Sci. 2010;365(1544):1203\u201312.","journal-title":"Philosophical Trans Royal Soc B: Biol Sci"},{"key":"6440_CR14","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3389\/fpls.2012.00198","volume":"3","author":"F Cheng","year":"2012","unstructured":"Cheng F, Wu J, Fang L, Wang X. Syntenic gene analysis between brassica rapa and other brassicaceae species. Front Plant Sci. 2012;3:198.","journal-title":"Front Plant Sci"},{"issue":"3","key":"6440_CR15","doi-asserted-by":"publisher","first-page":"534","DOI":"10.2307\/2657117","volume":"88","author":"M Koch","year":"2001","unstructured":"Koch M, Haubold B, Mitchell-Olds T. Molecular systematics of the brassicaceae: evidence from coding plastidic matK and nuclear chs sequences. Am J Bot. 2001;88(3):534\u201344.","journal-title":"Am J Bot"},{"issue":"6","key":"6440_CR16","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/nrg3920","volume":"16","author":"MW Libbrecht","year":"2015","unstructured":"Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321\u201332.","journal-title":"Nat Rev Genet"},{"issue":"6594","key":"6440_CR17","doi-asserted-by":"publisher","first-page":"eabl4290","DOI":"10.1126\/science.abl4290","volume":"376","author":"G Eraslan","year":"2022","unstructured":"Eraslan G, et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science. 2022;376(6594):eabl4290.","journal-title":"Science"},{"issue":"7","key":"6440_CR18","doi-asserted-by":"publisher","first-page":"878","DOI":"10.15252\/msb.20156651","volume":"12","author":"C Angermueller","year":"2016","unstructured":"Angermueller C, P\u00e4rnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878.","journal-title":"Mol Syst Biol"},{"issue":"1","key":"6440_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41588-018-0295-5","volume":"51","author":"J Zou","year":"2019","unstructured":"Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12\u20138. https:\/\/doi.org\/10.1038\/s41588-018-0295-5.","journal-title":"Nat Genet"},{"issue":"1","key":"6440_CR20","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B: Stat Methodol. 1996;58(1):267\u201388.","journal-title":"J Royal Stat Soc Ser B: Stat Methodol"},{"key":"6440_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21:1\u201313.","journal-title":"BMC Genomics"},{"issue":"5","key":"6440_CR22","first-page":"851","volume":"18","author":"S Min","year":"2017","unstructured":"Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017;18(5):851\u201369.","journal-title":"Brief Bioinform"},{"key":"6440_CR23","doi-asserted-by":"crossref","unstructured":"Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23(19):2507\u20132517.","DOI":"10.1093\/bioinformatics\/btm344"},{"issue":"1","key":"6440_CR24","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1146\/annurev.ecolsys.37.091305.110031","volume":"37","author":"MB Jones","year":"2006","unstructured":"Jones MB, Schildhauer MP, Reichman O, Bowers S. The new bioinformatics: Integrating ecological data from the gene to the biosphere. Annu Rev Ecol Evol Syst. 2006;37(1):519\u201344.","journal-title":"Annu Rev Ecol Evol Syst"},{"key":"6440_CR25","unstructured":"Vaswani A et al. Attention is all you need. Adv Neural Inf Process Syst. 30;2017."},{"key":"6440_CR26","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, et al. Graph neural networks: a review of methods and applications. AI open. 2020;1:57\u201381.","journal-title":"AI open"},{"key":"6440_CR27","unstructured":"Bishop CM, Nasrabadi NM. Pattern recognition and machine learning. Volume 4. Springer. 2006."},{"key":"6440_CR28","unstructured":"Provost F, Fawcett T. Data science for business: what you need to know about data mining and data-analytic thinking. O\u2019Reilly Media, Inc.; 2013."},{"issue":"3","key":"6440_CR29","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015;10(3):e0118432.","journal-title":"PLoS ONE"},{"key":"6440_CR30","unstructured":"Powers DM. Evaluation: From precision, recall and f-measure to ROC, informedness, markedness and correlation. 2020. arXiv:2010.16061"},{"key":"6440_CR31","unstructured":"Chinchor N. Proceedings of the 4th conference on message understanding, Association for Computational Linguistics McLean, Virginia, 1992."},{"key":"6440_CR32","doi-asserted-by":"crossref","unstructured":"Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct. 1975;405(2):442\u2013451.","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"6440_CR33","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.compbiolchem.2004.09.006","volume":"28","author":"J Gorodkin","year":"2004","unstructured":"Gorodkin J. Comparing two k-category assignments by a k-category correlation coefficient. Comput Biol Chem. 2004;28:5\u20136.","journal-title":"Comput Biol Chem"},{"issue":"6","key":"6440_CR34","doi-asserted-by":"publisher","first-page":"e0177678","DOI":"10.1371\/journal.pone.0177678","volume":"12","author":"S Boughorbel","year":"2017","unstructured":"Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using matthews correlation coefficient metric. PLoS ONE. 2017;12(6):e0177678.","journal-title":"PLoS ONE"},{"issue":"1","key":"6440_CR35","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55\u201367.","journal-title":"Technometrics"},{"key":"6440_CR36","doi-asserted-by":"crossref","unstructured":"James G, Witten D, Hastie T, Tibshirani R, et al. An introduction to statistical learning. Volume 112. Springer; 2013.","DOI":"10.1007\/978-1-4614-7138-7"},{"issue":"5","key":"6440_CR37","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3390\/computation13050127","volume":"13","author":"V Teodorescu","year":"2025","unstructured":"Teodorescu V, Obreja L, Bra\u0219oveanu. Assessing the validity of k-fold cross-validation for model selection: evidence from bankruptcy prediction using random forest and XGBoost. Computation. 2025;13(5):127. https:\/\/doi.org\/10.3390\/computation13050127.","journal-title":"Computation"},{"key":"6440_CR38","unstructured":"Krogh A, Hertz J. A simple weight decay can improve generalization. Adv Neural Inf Process Syst. 1991;4."},{"key":"6440_CR39","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u2013444.","DOI":"10.1038\/nature14539"},{"key":"6440_CR40","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pmlr, 2015, pp. 448\u2013456."},{"issue":"1","key":"6440_CR41","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929\u201358.","journal-title":"J Mach Learn Res"},{"issue":"2","key":"6440_CR42","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Stat Soc Ser B Stat Methodol. 2005;67(2):301\u201320.","journal-title":"J Royal Stat Soc Ser B: Stat Methodol"},{"key":"6440_CR43","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014. arXiv:1412.6980"},{"key":"6440_CR44","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Volume 1. MIT press Cambridge; 2016."},{"key":"6440_CR45","unstructured":"Loshchilov I, Hutter F. Decoupled weight decay regularization. 2017. arXiv:1711.05101"},{"key":"6440_CR46","unstructured":"Loshchilov I, Hutter F. Decoupled Weight Decay Regularization. In: International conference on learning representations (ICLR). 2019. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7."},{"issue":"1","key":"6440_CR47","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1089\/cmb.2021.0458","volume":"30","author":"DR Goulet","year":"2023","unstructured":"Goulet DR, Yan Y, Agrawal P, Waight AB, Mak AN, Zhu Y. Codon optimization using a recurrent neural network. J Comput Biol. 2023;30(1):70\u201381.","journal-title":"J Comput Biol"},{"issue":"3","key":"6440_CR48","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning (still) requires rethinking generalization. Commun ACM. 2021;64(3):107\u201315.","journal-title":"Commun ACM"},{"key":"6440_CR49","unstructured":"Chollet F. Deep Learning with Python. Simon & Schuster; 2021."},{"key":"6440_CR50","unstructured":"Abadi M et al. TensorFlow: a system for Large-Scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016;265\u2013283."},{"key":"6440_CR51","unstructured":"Dem\u0161ar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine learning research. 2006;7:1\u201330."},{"issue":"11","key":"6440_CR52","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","volume":"25","author":"PJ Cock","year":"2009","unstructured":"Cock PJ, et al. Biopython: Freely available python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422.","journal-title":"Bioinformatics"},{"key":"6440_CR53","doi-asserted-by":"crossref","unstructured":"Wright S. The \u2018effective number of codons\u2019 used in a gene. Gene. 1990;87(1):23\u201329.","DOI":"10.1016\/0378-1119(90)90491-9"},{"key":"6440_CR54","doi-asserted-by":"publisher","unstructured":"LaBella AL, Opulente DA, Steenwyk JL, Hittinger CT, Rokas A. Variation and selection on codon usage bias across an entire subphylum. PLOS Genet. 2019;15(7):1\u201325. https:\/\/doi.org\/10.1371\/journal.pgen.1008304","DOI":"10.1371\/journal.pgen.1008304"},{"key":"6440_CR55","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1093\/biomet\/52.3-4.591","volume":"52","author":"SS Shapiro","year":"1965","unstructured":"Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52:3\u20134.","journal-title":"Biometrika"},{"key":"6440_CR56","unstructured":"Levene H. Robust tests for equality of variances. Contrib Probab Stat, pp. 278\u201392, 1960."},{"key":"6440_CR57","unstructured":"Fisher RA. Statistical methods for research workers. 1934."},{"issue":"260","key":"6440_CR58","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47(260):583\u2013621.","journal-title":"J Am Stat Assoc"},{"key":"6440_CR59","unstructured":"Field A. Discovering statistics using IBM SPSS statistics. Sage publications limited; 2024."},{"key":"6440_CR60","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579\u20132605, 2008."},{"issue":"1","key":"6440_CR61","doi-asserted-by":"publisher","first-page":"5416","DOI":"10.1038\/s41467-019-13056-x","volume":"10","author":"D Kobak","year":"2019","unstructured":"Kobak D, Berens P. The art of using t-SNE for single-cell transcriptomics. Nat Commun. 2019;10(1):5416.","journal-title":"Nat Commun"},{"issue":"10","key":"6440_CR62","doi-asserted-by":"publisher","first-page":"e2","DOI":"10.23915\/distill.00002","volume":"1","author":"M Wattenberg","year":"2016","unstructured":"Wattenberg M, Vi\u00e9gas F, Johnson I. How to use t-SNE effectively. Distill. 2016;1(10):e2.","journal-title":"Distill"},{"issue":"1","key":"6440_CR63","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/nbt.4314","volume":"37","author":"E Becht","year":"2019","unstructured":"Becht E, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37(1):38\u201344.","journal-title":"Nat Biotechnol"},{"key":"6440_CR64","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. 2018. arXiv:1802.03426","DOI":"10.21105\/joss.00861"},{"issue":"2","key":"6440_CR65","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41587-020-00809-z","volume":"39","author":"D Kobak","year":"2021","unstructured":"Kobak D, Linderman GC. Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nat Biotechnol. 2021;39(2):156\u20137.","journal-title":"Nat Biotechnol"},{"key":"6440_CR66","unstructured":"Keskar NS, Mudigere D, Nocedal J, Smelyanskiy M, Tang PTP. On large-batch training for deep learning: Generalization gap and sharp minima. 2016. arXiv:1609.04836"},{"key":"6440_CR67","doi-asserted-by":"crossref","unstructured":"Feurer M, Hutter F. Automated machine learning. Cham: Springer, 2019;113\u2013134.","DOI":"10.1007\/978-3-030-05318-5_6"},{"issue":"7","key":"6440_CR68","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","volume":"20","author":"G Eraslan","year":"2019","unstructured":"Eraslan G, Avsec \u017d, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20(7):389\u2013403.","journal-title":"Nat Rev Genet"},{"issue":"4","key":"6440_CR69","doi-asserted-by":"publisher","first-page":"2609","DOI":"10.1007\/s10462-020-09910-w","volume":"54","author":"AG Nath","year":"2021","unstructured":"Nath AG, Udmale SS, Singh SK. Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artif Intell Rev. 2021;54(4):2609\u201368.","journal-title":"Artif Intell Rev"},{"key":"6440_CR70","doi-asserted-by":"crossref","unstructured":"Smith LN, Topin N. Super-convergence: very fast training of neural networks using large learning rates. In: Artificial intelligence and machine learning for multi-domain operations applications. SPIE; 2019. pp. 369\u201386.","DOI":"10.1117\/12.2520589"},{"issue":"8","key":"6440_CR71","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013;35(8):1798\u2013828.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6440_CR72","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s11517-020-02292-9","volume":"59","author":"X Zhang","year":"2021","unstructured":"Zhang X, Li J, Cai Z, Zhang L, Chen Z, Liu C. Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection. Med Biol Eng Comput. 2021;59:165\u201373.","journal-title":"Med Biol Eng Comput"},{"key":"6440_CR73","doi-asserted-by":"crossref","unstructured":"Bengio Y. Practical recommendations for gradient-based training of deep architectures. In: Neural networks: tricks of the trade: second edition. Springer. 2012;437\u201378.","DOI":"10.1007\/978-3-642-35289-8_26"},{"issue":"6","key":"6440_CR74","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1162\/neco_a_01086","volume":"30","author":"F Zenke","year":"2018","unstructured":"Zenke F, Ganguli S, Superspike. Supervised learning in multilayer spiking neural networks. Neural Comput. 2018;30(6):1514\u201341.","journal-title":"Neural Comput"},{"issue":"1","key":"6440_CR75","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13(1):281\u2013305.","journal-title":"J Mach Learn Res"},{"issue":"10","key":"6440_CR76","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1038\/ng.919","volume":"43","author":"B. rapa Genome Sequencing Project Consortium","year":"2011","unstructured":"B. rapa Genome Sequencing Project Consortium. The genome of the mesopolyploid crop species brassica rapa. Nat Genet. 2011;43(10):1035\u20139.","journal-title":"Nat Genet"},{"issue":"3","key":"6440_CR77","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.cell.2019.01.001","volume":"176","author":"E Pasolli","year":"2019","unstructured":"Pasolli E, et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell. 2019;176(3):649\u201362.","journal-title":"Cell"},{"key":"6440_CR78","doi-asserted-by":"crossref","unstructured":"Chalhoub B et al. Early allopolyploid evolution in the post-neolithic brassica napus oilseed genome. Science. 2014;345(6199):950\u2013953.","DOI":"10.1126\/science.1253435"},{"key":"6440_CR79","doi-asserted-by":"crossref","unstructured":"Gutman GA, Hatfield GW. Nonrandom utilization of codon pairs in escherichia coli. In: Proceedings of the national academy of sciences. 1989;86(10):3699\u20133703.","DOI":"10.1073\/pnas.86.10.3699"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-026-06440-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06440-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06440-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T06:28:08Z","timestamp":1779172088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12859-026-06440-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,11]]},"references-count":79,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6440"],"URL":"https:\/\/doi.org\/10.1186\/s12859-026-06440-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,11]]},"assertion":[{"value":"16 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2026","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":"Consent for publication"}},{"value":"and consent participation.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"109"}}