Simultaneous estimation following subset selection of binomial populations

METRON ◽  
2012 ◽  
Vol 70 (1) ◽  
pp. 59-69
Author(s):  
Riyadh Rustam Al-mosawi
Keyword(s):  
Subset Selection ◽  
Simultaneous Estimation ◽  
Selection Of ◽  
Binomial Populations

scholarly journals A predictive equation for residual strength using a hybrid of Subset Selection of maximum dissimilarity method with Pareto optimal multi-gene genetic programming

2021 ◽  
pp. 101222
Author(s):  
Hossien Riahi-Madvar ◽  
Mahsa Gholami ◽  
Bahram Gharabaghi ◽  
Seyed Morteza Seyedian
Keyword(s):  
Genetic Programming ◽  
Residual Strength ◽  
Subset Selection ◽  
Pareto Optimal ◽  
Predictive Equation ◽  
Selection Of

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.

Goal-oriented optimal subset selection of correlated multimedia streams

2007 ◽  
Vol 3 (1) ◽  
pp. 2 ◽  
Author(s):  
Pradeep K. Atrey ◽  
Mohan S. Kankanhalli ◽  
John B. Oommen
Keyword(s):  
Subset Selection ◽  
Optimal Subset ◽  
Multimedia Streams ◽  
Selection Of

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.

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