{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:31:43Z","timestamp":1781659903131,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bill &amp; Melinda Gates Foundation","award":["INV-003439 BMGF\/FCDO"],"award-info":[{"award-number":["INV-003439 BMGF\/FCDO"]}]},{"name":"Bill &amp; Melinda Gates Foundation","award":["Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat\/AGGMW, Genes 2023, 14, 927 14 of 18 AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)"],"award-info":[{"award-number":["Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat\/AGGMW, Genes 2023, 14, 927 14 of 18 AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)"]}]},{"name":"USAID projects","award":["INV-003439 BMGF\/FCDO"],"award-info":[{"award-number":["INV-003439 BMGF\/FCDO"]}]},{"name":"USAID projects","award":["Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat\/AGGMW, Genes 2023, 14, 927 14 of 18 AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)"],"award-info":[{"award-number":["Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat\/AGGMW, Genes 2023, 14, 927 14 of 18 AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction.<\/jats:p>","DOI":"10.3390\/a17060260","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T03:52:53Z","timestamp":1718337173000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars"],"prefix":"10.3390","volume":"17","author":[{"given":"Osval A.","family":"Montesinos-L\u00f3pez","sequence":"first","affiliation":[{"name":"Facultad de Telem\u00e1tica, Universidad de Colima, Colima 28040, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvinth","family":"Sivakumar","sequence":"additional","affiliation":[{"name":"ICAR\u2014Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3867-4935","authenticated-orcid":false,"given":"Gloria Isabel","family":"Huerta Prado","sequence":"additional","affiliation":[{"name":"Independent Researcher, Zinacatepec 75960, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4465-325X","authenticated-orcid":false,"given":"Josafhat","family":"Salinas-Ruiz","sequence":"additional","affiliation":[{"name":"Colegio de Postgraduados Campus C\u00f3rdoba, Km. 348 Carretera Federal C\u00f3rdoba-Veracruz, Amatl\u00e1n de los Reyes, Veracruz 94946, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afolabi","family":"Agbona","sequence":"additional","affiliation":[{"name":"International Institute of Tropical Agriculture (IITA), Ibadan 200001, Nigeria"},{"name":"Molecular & Environmental Plant Sciences, Texas A&M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel Efra\u00edn","family":"Ortiz Reyes","sequence":"additional","affiliation":[{"name":"Facultad de Telem\u00e1tica, Universidad de Colima, Colima 28040, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5760-0216","authenticated-orcid":false,"given":"Khalid","family":"Alnowibet","sequence":"additional","affiliation":[{"name":"Department of Statistics and Operations Research, King Saud University, Riyah 11459, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1739-7206","authenticated-orcid":false,"given":"Rodomiro","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Department of Plant Breeding, Swedish University of Agricultural Science (SLU), P.O. 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