| 000 | 02363nab|a22003617a|4500 | ||
|---|---|---|---|
| 999 |
_c59646 _d59638 |
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| 001 | 59646 | ||
| 003 | MX-TxCIM | ||
| 005 | 20240919020950.0 | ||
| 008 | 180728s2018||||wiu|||p|op||||00||0|eng|d | ||
| 024 | 8 | _ahttps://doi.org/10.3835/plantgenome2017.11.0104 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_95411 _aGonzález-Camacho, J.M. |
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| 245 | 1 | _aApplications of machine learning methods to genomic selection in breeding wheat for rust resistance | |
| 260 |
_aMadison, U.S. : _bCrop Science Society of America, _c2018. |
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| 500 | _aOpen Access | ||
| 500 | _aPeer review | ||
| 520 | _aNew 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?5, 1?9, 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-of-the-art 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. | ||
| 546 | _aText in English | ||
| 591 | _aCIMMYT Informa : 2019 (September 13, 2018) | ||
| 650 | 7 |
_2AGROVOC _95832 _aGenomic features |
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| 650 | 7 |
_aBreeding _gAGROVOC _2 _91029 |
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| 650 | 7 |
_aWheat _gAGROVOC _2 _91310 |
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| 650 | 7 |
_aPlant diseases _gAGROVOC _2 _91206 |
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| 700 | 1 |
_95410 _aOrnella, L. |
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| 700 | 1 |
_92703 _aPerez-Rodriguez, P. |
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| 700 | 1 |
_aGianola, D. _97797 |
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| 700 | 1 |
_9851 _aDreisigacker, S. _gGlobal Wheat Program _8INT2692 |
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| 700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
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| 773 | 0 |
_dCrop Science Society of America, 2018 _gv. 11, no. 2, art. 170104 _tPlant Genome _wu94757 _x1940-3372 |
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| 856 | 4 |
_uhttps://repository.cimmyt.org/handle/10883/19573 _yOpen Access through DSpace |
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| 942 |
_cJA _2ddc _n0 |
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