scholarly journals PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 230-231
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecan ◽  
Kadir Kizilkaya ◽  
Milt Thomas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Network Models ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Neural Network Models ◽  
Bivariate Normal ◽  
Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

scholarly journals PSVIII-20 Univariate Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios using Artificial Neural Network

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 264-265
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecen ◽  
Kadir Kizilkaya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Bivariate Normal ◽  
Artificial Neural ◽  
Snp Panels ◽  
Genomic Predictions

Abstract A simulation study was carried out to determine the likely accuracy of genomic prediction from univariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by univariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations between phenotypes and predicted phenotypes from 10-fold cross validation for T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of univariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that genomic predictions from the trait with high heritability can achieve higher correlation than those from the trait with low heritability. Panels including SNP markers perform better prediction than Panel1 and have a greater chance of including markers in LD with QTL and allow the possibility of predicting each QTL from collective action of several markers.

scholarly journals PSVIII-21 Bivariate genomic prediction with different heritability, QTL and SNP panel scenarios using artificial neural network

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 261-262
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecen ◽  
Kadir Kizilkaya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Breeding Value ◽  
Bivariate Normal ◽  
Artificial Neural ◽  
Snp Panels ◽  
Genomic Predictions

Abstract A bivariate simulation study was carried out to compare the accuracies of genomic predictions from bivariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons with three sets of SNP panels, only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). The correlations between phenotypes and predicted phenotypes from 10-fold cross validation for bivariate analysis of T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of bivariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Correlations for genomic predictions of T2h2=0.5 were higher than those from T2h2=0.25 for all QTL and Panel scenarios (Table 1). Panle2 including QTL and SNP performs better prediction than Panel1 and Panel3 in QTL100, QTL250 and QTL500 scenarios and the correlation from Panel3 including only SNP, which is more realistic, are similar to or higher than those from Panel1.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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