{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:13:54Z","timestamp":1790014434539,"version":"4.0.1"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Genome prediction is a breeding strategy that predicts crop phenotype through genomic markers. Traditional statistical models cannot accurately capture the complex relationships between genotypes and phenotypes, and deep neural networks have shown significant advantages in capturing nonlinear relationships. Deep learning methods in genome selection primarily utilize sequential convolutional kernels, which can lead to challenges such as gradient disappearance and computational inefficiency. In this study a novel crop genomic prediction method based on a parallel convolutional attention network (PCAGP) is proposed. The constructed deep learning framework uses the convolution kernel to process information through a parallel structure, combined with a coordinate attention mechanism that effectively integrates inter-channel relationships and spatial genomic information. PCAGP was compared with eight widely used genomic prediction methods on four benchmark datasets, including a traditional statistical method, three machine learning methods, and four popular deep learning methods. The experimental results demonstrate that PCAGP outperforms all baseline methods and significantly improves genomic prediction accuracy.<\/jats:p>","DOI":"10.1371\/journal.pone.0358451","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:38:07Z","timestamp":1790012287000},"page":"e0358451","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A parallel convolution attention network for crop genomic prediction"],"prefix":"10.1371","volume":"21","author":[{"given":"Wenyu","family":"Peng","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University of Science and Technology","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6081-1759","authenticated-orcid":true,"given":"Yuxia","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Electronic 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Meuwissen","year":"2001","journal-title":"Genetics"},{"issue":"2","key":"pone.0358451.ref006","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1093\/bfgp\/elq001","article-title":"Genomic selection in plant breeding: from theory to practice","volume":"9","author":"J-L Jannink","year":"2010","journal-title":"Brief Funct Genomics"},{"issue":"3","key":"pone.0358451.ref007","first-page":"1","article-title":"Research progress in genomic selection breeding technology for crops","volume":"40","author":"X Wang","year":"2024","journal-title":"Biotechnology Bulletin"},{"issue":"5","key":"pone.0358451.ref008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0323140","article-title":"Genome-wide association analysis and genomic selection for leaf-related traits of maize","volume":"20","author":"Y Zeng","year":"2025","journal-title":"PLoS 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genetic algorithm to improve the predictability of genomic selection","volume":"25","author":"Y Xu","year":"2024","journal-title":"Brief Bioinform"},{"issue":"4","key":"pone.0358451.ref013","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae276","article-title":"Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information","volume":"25","author":"J Teng","year":"2024","journal-title":"Brief Bioinform"},{"issue":"18","key":"pone.0358451.ref014","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.1093\/bioinformatics\/btab212","article-title":"L2,1-norm regularized multivariate regression model with applications to genomic prediction","volume":"37","author":"AJ Mbebi","year":"2021","journal-title":"Bioinformatics"},{"issue":"11","key":"pone.0358451.ref015","doi-asserted-by":"crossref","DOI":"10.1093\/g3journal\/jkac226","article-title":"learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data","volume":"12","author":"CC Westhues","year":"2022","journal-title":"G3 (Bethesda)"},{"issue":"1","key":"pone.0358451.ref016","article-title":"Multigenerational prediction of genetic values using genome-enabled prediction","volume":"14","author":"C Sant\u2019 Anna I de","year":"2019","journal-title":"PLoS One"},{"issue":"7","key":"pone.0358451.ref017","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.tplants.2024.12.009","article-title":"Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software","volume":"30","author":"J Crossa","year":"2025","journal-title":"Trends Plant Sci"},{"key":"pone.0358451.ref018","doi-asserted-by":"crossref","first-page":"685488","DOI":"10.3389\/fpls.2021.685488","article-title":"Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize","volume":"12","author":"Y Beyene","year":"2021","journal-title":"Front Plant Sci"},{"issue":"1","key":"pone.0358451.ref019","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.molp.2022.11.004","article-title":"DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants","volume":"16","author":"K Wang","year":"2023","journal-title":"Mol Plant"},{"issue":"6","key":"pone.0358451.ref020","article-title":"SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding","volume":"24","author":"P Gao","year":"2023","journal-title":"Brief Bioinform"},{"issue":"3","key":"pone.0358451.ref021","doi-asserted-by":"crossref","first-page":"101223","DOI":"10.1016\/j.xplc.2024.101223","article-title":"Cropformer: An interpretable deep learning framework for crop genomic prediction","volume":"6","author":"H Wang","year":"2025","journal-title":"Plant 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genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms","volume":"18","author":"Z Yao","year":"2025","journal-title":"Mol Plant"},{"key":"pone.0358451.ref026","doi-asserted-by":"crossref","first-page":"1410596","DOI":"10.3389\/fpls.2024.1410596","article-title":"PNNGS, a multi-convolutional parallel neural network for genomic selection","volume":"15","author":"Z Xie","year":"2024","journal-title":"Front Plant Sci"},{"key":"pone.0358451.ref027","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J. Coordinate Attention for Efficient Mobile Network Design. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 13708\u201317. https:\/\/doi.org\/10.1109\/cvpr46437.2021.01350","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"pone.0358451.ref028","unstructured":"Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference on Machine Learning, 2015. 448\u201356."},{"key":"pone.0358451.ref029","first-page":"873","article-title":"Comparative Study of Convolution Neural Network\u2019s Relu and Leaky-Relu Activation Functions","volume-title":"Lecture Notes in Electrical Engineering","author":"AK Dubey","year":"2019"},{"key":"pone.0358451.ref030","doi-asserted-by":"crossref","unstructured":"Meyer GP. An Alternative Probabilistic Interpretation of the Huber Loss. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 5257\u201365. https:\/\/doi.org\/10.1109\/cvpr46437.2021.00522","DOI":"10.1109\/CVPR46437.2021.00522"},{"issue":"1","key":"pone.0358451.ref031","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1186\/s12870-020-02604-z","article-title":"Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study","volume":"20","author":"B Karikari","year":"2020","journal-title":"BMC Plant Biol"},{"key":"pone.0358451.ref032","doi-asserted-by":"crossref","unstructured":"Postman J, Hummer K, Ayala-Silva T, Bretting P, Franko T, Kinard G. GRIN-Global: an international project to develop a global plant genebank information management system. In: 2009. 49\u201355. https:\/\/doi.org\/10.17660\/actahortic.2010.859.4","DOI":"10.17660\/ActaHortic.2010.859.4"},{"key":"pone.0358451.ref033","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkp798","article-title":"SoyBase, the USDA-ARS soybean genetics and genomics database","volume":"38","author":"D Grant","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"pone.0358451.ref034","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1534\/g3.116.029637","article-title":"Genomic Prediction of Gene Bank Wheat Landraces","volume":"6","author":"J Crossa","year":"2016","journal-title":"G3 (Bethesda)"},{"key":"pone.0358451.ref035","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkad1062","article-title":"CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops","volume":"52","author":"J Chen","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"pone.0358451.ref036","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.procs.2022.01.100","article-title":"House Price Prediction using Random Forest Machine Learning Technique","volume":"199","author":"AB Adetunji","year":"2022","journal-title":"Procedia Computer Science"},{"issue":"7","key":"pone.0358451.ref037","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s00122-007-0627-9","article-title":"Support vector machine regression for the prediction of maize hybrid performance","volume":"115","author":"S Maenhout","year":"2007","journal-title":"Theor Appl Genet"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:38:17Z","timestamp":1790012297000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358451"}},"subtitle":[],"editor":[{"given":"Xiaoyong","family":"Sun","sequence":"first","affiliation":[{"name":"Shandong Agricultural University","place":["CHINA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,21]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0358451","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}