scholarly journals Subset selection of markers for genome-enabled prediction of genetic val-ues using radial basis function neural networks

2018 ◽  
Author(s):  
Isabela de Castro Sant' Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damiao Cruz
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Subset Selection ◽  
Predictive Ability ◽  
Phenotypic Traits ◽  
Radial Basis ◽  
The Mean ◽  
Selection Of

This paper aimed to evaluate the efficiency of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). For this purpose, an F1 population from hybridization of divergent parents with 500 individuals geno-typed with 1,000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistasic , com-plying with two dominance situations: partial and complete with quantitative traits admitting heritability (h2) equal to 30 and 60%, each one controlled by 50 loci, considering two alleles per locus, totaling 12 different scenarios. To evaluate the predictive ability of RR_BLUP and the neural networks, a cross-validation procedure with five replicates were trained using 80% of the individuals of the population. Two methods were used: dimensionality reduction and stepwise regression. The square of the correlation between the predicted genomic estimated breeding val-ue (GEBV) and the phenotype value was used to measure predictive reliability. For h2 = 0.3 in the additive scenario, the R2 values were 59% for neural network (RBFNN) and 57% for RR-BLUP, and in the epistatic scenario, R2 values were 50% and 41%, respectively. Additionally, when analyzing the mean-squared error root, the difference in performance between the tech-niques is even greater. For the additive scenario, the estimates were 91 for RR-BLUP and 5 for neural networks and, in the most critical scenario, they were 427 for RR-BLUP and 20 for neu-ral network. The results showed that the use of neural networks and variable selection tech-niques allows capturing epistasis interactions, leading to an improvement in the accuracy of pre-diction of the genetic value and, mainly, to a large reduction of the mean square error, which indicates greater genomic value.

scholarly journals Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

2020 ◽  
Vol 43 ◽  
pp. e46307 ◽  
Author(s):  
Isabela de Castro Sant'Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damião Cruz
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basis Function ◽  
Stepwise Regression ◽  
Subset Selection ◽  
Mean Square ◽  
Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

scholarly journals A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape

2018 ◽  
Vol 31 (2) ◽  
pp. 210
Author(s):  
Laith Jasim Saud ◽  
Zainab Kudair Abass
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Rbf Neural Network ◽  
Human Detection ◽  
Geometric Information ◽  
Radial Basis ◽  
Mlp Model

       Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.

Regularization in the Selection of Radial Basis Function Centers

1995 ◽  
Vol 7 (3) ◽  
pp. 606-623 ◽  
Author(s):  
Mark J. L. Orr
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Regularization Parameter ◽  
Subset Selection ◽  
Simulation Studies ◽  
Forward Selection ◽  
Generalization Performance ◽  
Regression Estimators ◽  
Radial Basis ◽  
Selection Of

Subset selection and regularization are two well-known techniques that can improve the generalization performance of nonparametric linear regression estimators, such as radial basis function networks. This paper examines regularized forward selection (RFS)—a combination of forward subset selection and zero-order regularization. An efficient implementation of RFS into which either delete-1 or generalized cross-validation can be incorporated and a reestimation formula for the regularization parameter are also discussed. Simulation studies are presented that demonstrate improved generalization performance due to regularization in the forward selection of radial basis function centers.

scholarly journals Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

Minerals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 131 ◽  
Author(s):  
Cyril Juliani ◽  
Steinar Ellefmo
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Input Data ◽  
Mean Squared Error ◽  
Training Data ◽  
Northern Norway ◽  
Radial Basis ◽  
Host Rocks ◽  
Selection Of

In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysical data, including up to 86 known mineral occurrences hosted in mafic host-rocks, were combined at different resolutions. Mineral occurrences were integrated into “deposit” and “non-deposit” training sets. Running RBFNN on different input vectors, with a k-fold cross-validation method, showed that increasing the number of iterations and radial basis functions resulted in: (1) a reduction of training mean squared error (MSE) down to 0.1, depending on the grid resolution, and (2) reaching correct classification rates of 0.9 and 0.6 for training and validation, respectively. The latter depends on: (1) the selection of “non-deposit” training data throughout the study area, (2) the scale at which data was acquired, and (3) the dissimilarity of input vectors. The “deposit” input data were correctly identified by the trained model (up to 83%) after proceeding to classification of non-training data. Up to 885 km2 of the Finnmark region studied is favorable for Cu mineralization based on the resulting mineral prospectivity map. The prospectivity map can be used as a reconnaissance guide for future detailed ground surveys.

scholarly journals Radial basis function regression methods for predicting quantitative traits using SNP markers

2010 ◽  
Vol 92 (3) ◽  
pp. 209-225 ◽  
Author(s):  
NANYE LONG ◽  
DANIEL GIANOLA ◽  
GUILHERME J. M. ROSA ◽  
KENT A. WEIGEL ◽  
ANDREAS KRANIS ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Quantitative Traits ◽  
Mean Squared Error ◽  
Additive Model ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Snp Markers ◽  
Regression Methods ◽  
Radial Basis

SummaryA challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype–phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype–phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0·1, 0·25, 0·5, 0·75 and 0·9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive+dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype–phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.

Wind turbine power curve modeling using radial basis function neural networks and tabu search

2021 ◽  
Vol 163 ◽  
pp. 2137-2152
Author(s):  
Despina Karamichailidou ◽  
Vasiliki Kaloutsa ◽  
Alex Alexandridis
Keyword(s):  
Neural Networks ◽  
Tabu Search ◽  
Radial Basis Function ◽  
Wind Turbine ◽  
Basis Function ◽  
Power Curve ◽  
Radial Basis ◽  
Curve Modeling

Accurate fault locator for EHV transmission lines based on radial basis function neural networks

2004 ◽  
Vol 71 (3) ◽  
pp. 195-202 ◽  
Author(s):  
M. Joorabian ◽  
S.M.A. Taleghani Asl ◽  
R.K. Aggarwal
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Transmission Lines ◽  
Basis Function ◽  
Radial Basis
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