Neural networks for predicting breeding values and genetic gains

Silva, Gabi Nunes; Tomaz, Rafael Simões; Sant’Anna, Isabela de Castro; Nascimento, Moysés; Bhering, Leonardo Lopes; Cruz, Cosme Damião

doi:10.1590/0103-9016-2014-0057

Cited by 46 publications

(45 citation statements)

References 8 publications

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“…In addition, due to their non-linear structure (Haykin, 2009) ANNs can capture more complex features of data sets and do not require detailed information about the process to be modeled due to its self-learning (Nascimento et al, 2013;Silva et al, 2014;Sant'Anna et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…Subsequently, the simulated genotypes are used to train and validate neural networks. Thus, by the training of ANNs, the identifying the stable genotypes is not only executed based on the studied genotypes, but for a large collection of simulated genotypes according to predefined groups (Nascimento et al, 2013;Silva et al, 2014;Sant'Anna et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Correa¹,

Teodoro

Gonçalves

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Correa¹,

Teodoro

Gonçalves

et al. 2016

Genet. Mol. Res.

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“…For training purposes of the ANNs, data were amplified in a process described by Silva et al (2014), in the case of data obtained from experiments in a randomized complete block design. Information of 2600 genotypes was generated for the ANN training set.…”

Section: Simulated Amplification Of Experimental Data For Network Tramentioning

confidence: 99%

Superiority of artificial neural networks for a genetic classification procedure

et al. 2015

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ABSTRACT. The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

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“…This methodology has been widely used in agriculture, and it can go beyond human capacity to evaluate large data banks and relate them to a specific desirable characteristic. ANN have been used in simulation studies to predict genetic values (Silva et al, 2014;Peixoto et al, 2015), and in association with genomic analysis (Gianola et al, 2011;Ehret et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Alves

Tomaz

Laurindo

et al. 2017

Sci. agric. (Piracicaba, Braz.)

Self Cite

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Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications.In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes.However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

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Neural networks for predicting breeding values and genetic gains

Cited by 46 publications

References 8 publications

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability

Superiority of artificial neural networks for a genetic classification procedure

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

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