Resultados de búsqueda - "Prediction

Genomic Selection model to predict grain Zn concentrations por CGIAR Research Program on Rice

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Surveillance tool for improving disease control: Predictive mapping por CGIAR Research Program on Agriculture for Nutrition and Health

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Analytical approach for predicting potential areas of agroforestry expansion por CGIAR Research Program on Climate Change, Agriculture and Food Security

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Economic shocks predict increases in child wasting prevalence por Headey, Derek D., Ruel, Marie T.

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Methylation in the CHH context allows to predict recombination in rice por Peñuela, Mauricio, Gallo-Franco, Jenny Johana, Finke, Jorge, Rocha, Camilo, Gkanogiannis, Anestis, Ghneim-Herrera, Thaura, Lorieux, Mathias

“…Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. …”
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Space-time prediction of maize yields in Southern Africa por Muthoni, Francis K., Parente, Leandro, Thierfelder, Christian L., Diemen, Chris van, Hengl, Tomislov

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Prediction of genetic gains in sweet potato genotypes by polycross por Mariano, G.G., Pavan, B.E., Otoboni, M.E.F., Andrade, M.I., Vargas, P.F.

“…The direct selection enabled a higher prediction of gains for each trait, but the joint analysis maximized the selection gains for all traits of interest. …”
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Predicting technology adoption to improve research priority-setting por Batz, Franz-Jozef, Janssen, Willem G., Peters, Kurt J.

“…This paper presents an improved approach for predicting the speed and ceiling of technology adoption, which is a crucial information for research priority setting. …”
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Multivariate random forest prediction of poverty and malnutrition prevalence por Browne, Chris, Matteson, David S., McBride, Linden, Hu, Leiqiu, Liu, Yanyan, Sun, Ying, Wen, Jiaming, Barrett, Christopher B.

“…Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.…”
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Genomic Prediction in Faba bean for Heat and Herbicide Tolerance por Abou-Khater, Lynn, Maalouf, Fouad, Hamwieh, Aladdin, Jighly, Abdul-Qader, Joukhadar, Reem, Alsamman, Alsamman M., Ahmed, Zayed Babiker Mahgoub, Balech, Rind, Hu, Jinguo, Ma, Y., Sanchez-Garcia, Miguel, Agrawal, Shiv Kumar

Materias: “…genomic prediction…”
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Data augmentation enhances plant-genomic-enabled predictions por Montesinos-Lopez, Osval A., Solis-Camacho, Mario Alberto, Crespo Herrera, Leonardo A., Saint Pierre, Carolina, Huerta Prado, Gloria Isabel, Ramos-Pulido, Sofia, Al-Nowibet, Khalid, Fritsche-Neto, Roberto, Gerard, Guillermo S., Montesinos-Lopez, Abelardo, Crossa, José

“…We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the top lines in the 14 datasets under study. …”
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Predicting farmer demand for transgenic cooking bananas in Uganda por Edmeades, Svetlana, Smale, Melinda

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Predicting rice phenotypes with meta and multi-target learning por Orhobor, Oghenejokpeme I., Alexandrov, Nickolai N., King, Ross D.

“…Furthermore, we make comparisons to multi-target learning, given that one is typically interested in predicting multiple phenotypes. We evaluated the frameworks and multi-target learning approaches on a genomic rice dataset where the regression task is to predict plant phenotype. …”
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Characterization of statistical features for plant microRNA prediction por Thakur, Vivek, Wanchana, Samart, Xu, Mercedes, Bruskiewich, Richard, Quick, William Paul, Mosig, Axel, Zhu, Xinguang

“…In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.…”
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Enhancing across-population genomic prediction for maize hybrids por Guangning Yu, Furong Li, Xin Wang, Yuxiang Zhang, Kai Zhou, Wenyan Yang, Xiusheng Guan, Xuecai Zhang, Chenwu Xu, Yang Xu

“…In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. …”
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Predicting feed quality—chemical analysis and in vitro evaluation por Mould, F.L

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Predicted impact of climate change on coffee-supply chains por Läderach, Peter R.D., Lundy, Mark M., Jarvis, Andy, Ramírez Villegas, Julián Armando, Pérez, P.E., Schepp, Kathleen, Eitzinger, Anton

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Models for short term malaria prediction in Sri Lanka por Briet, Olivier J.T., Vounatsou, Penelope, Gunawardena, Dissanayake M., Galappaththy, Gawrie N.L., Amerasinghe, Priyanie H.

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Predicted impact of climate change on coffee-supply chains por Läderach, Peter R.D., Lundy, Mark M., Jarvis, Andy, Ramírez Villegas, Julián Armando, Pérez Portilla, E., Schepp, Kathleen

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Evaluation of two GIS-based models for landslide prediction por Walther, S, Oberthür, Thomas, Rubiano Mejía, Jorge Eliécer, Schultze-Kraft, Rainer

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