{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T14:13:27Z","timestamp":1793974407774,"version":"4.8.0"},"reference-count":69,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["acsess.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Plant Genome"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:sec>\n                    <jats:label\/>\n                    <jats:p>New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1\u20135, 1\u20139, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state\u2010of\u2010the\u2010art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3835\/plantgenome2017.11.0104","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T15:21:38Z","timestamp":1523546498000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":154,"title":["Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance"],"prefix":"10.1002","volume":"11","author":[{"given":"Juan Manuel","family":"Gonz\u00e1lez\u2010Camacho","sequence":"first","affiliation":[{"name":"Statistics and Computer Science Graduate Program Colegio de Postgraduados, Montecillo, Texcoco M\u00e9xico CP 56230"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Ornella","sequence":"additional","affiliation":[{"name":"NIDERA SA 2600 Venado Tuerto Argentina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulino","family":"P\u00e9rez\u2010Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Statistics and Computer Science Graduate Program Colegio de Postgraduados, Montecillo, Texcoco M\u00e9xico CP 56230"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Gianola","sequence":"additional","affiliation":[{"name":"Dep. of Animal Sciences, Dairy Science, and Biostatistics and Medical Informatics Univ. of Wisconsin Madison WI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanne","family":"Dreisigacker","sequence":"additional","affiliation":[{"name":"Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT) Apdo. 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