Person: Singh, R.P.
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Singh
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R.P.
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Singh, R.P.
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0000-0002-4676-50716 results
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- Bayesian multitrait kernel methods improve multienvironment genome-based prediction(Oxford University Press, 2022) Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-López, A.; Ramirez-Alcaraz, J.M.; Poland, J.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J.
Publication - Increased predictive accuracy of multi-environment genomic prediction model for yield and related traits in spring wheat (Triticum aestivum L.)(Frontiers, 2021) Tomar, V.; Singh, D.; Dhillon, G.S.; Yong Suk Chung; Poland, J.; Singh, R.P.; Joshi, A.K.; Gautam, Y.; Tiwari, B.S.; Kumar, U.
Publication - Pre-emptive breeding against karnal bunt infection in common wheat: combining genomic and agronomic information to identify suitable parents(Frontiers, 2021) Emebiri, L.C.; Hildebrand, S.; Tan, M.K.; Juliana, P.; Singh, P.K.; Fuentes Dávila, G.; Singh, R.P.
Publication - Retrospective quantitative genetic analysis and genomic prediction of global wheat yields(Frontiers, 2020) Juliana, P.; Singh, R.P.; Braun, H.J.; Huerta-Espino, J.; Crespo Herrera, L.A.; Payne, T.S.; Poland, J.; Shrestha, S.; Kumar, U.; Joshi, A.K.; Imtiaz, M.; Rahman, M.M.; Toledo, F.H.
Publication - New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes(Genetics Society of America, 2019) Montesinos-Lopez, O.A.; Martin Vallejo, F.J.; Crossa, J.; Gianola, D.; Hernandez-Suarez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P.Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype x environment (GxE) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.
Publication - A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding(Genetics Society of America, 2019) Montesinos-Lopez, O.A.; Martin Vallejo, F.J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P.Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.
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