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ABSTRACT: The complex Interconnections between markers and polygenic genotype value suggested that the regression was not enough for describing the relation between genes and traits. Artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems. Recently, artificial neural networks had been successfully used to predict the polygenic genotype value, and the different learning rate and hidden neurons number were used to discuss the influencing of the learning rate on estimating the polygenic genotype value. However, when optimazing the structure of BP-artificial neural networks, a series of networks with an variable number of hidden neurons and input neuron needs to be optimized, compared and selected, the elapsed time could be very long, therefore the elapsed time was very important for work efficients. In this paper, the influence of different gene parameter nomorlization on back-propagation artificial neural network caculating time of animal phenotype value pretiction was discussed. The results showed that the caculating time could be affected by many gene parameters, such as the gene effect, gene locus number, gene frequency, and their nomorlization, the normorlization can improve the training speed and induce absolute time or elapsed time very obviously. These suggested that normalizing the phenotype value was an very important method for improving our work efficient.
2011 Fourth International Conference on Information and Computing. 06/2010; 2:90-93.
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ABSTRACT: In the past, a prediction equation based on the single nucleotide polymorphisms (SNP) is derived to calculate genomic breeding values (GEBV). However, the genome is very complex; a function could not reflect the relation between markers and phenotypes. Unlike the methods of regression, artificial neural networks (ANNs) could perform well for optimization in complex non-linear systems, however, artificial neural networks (ANNs) have not been used to calculate genomic breeding values (CEBV).In this paper, back-propagation neural network is used to predict the genomic breeding values (GEBV) or polygenic genotype value, and the different learning rate and hidden neurons number were used to discuss the influencing of the learning rate on estimating the polygenic genotype value. The result showed artificial neural networks could gather knowledge by detecting the relations between molecular marker polymorphism and phenotype value, and could predict the animal polygenic genotype value or breeding values as well as the molecular marker genotype being given. Training speed, prediction accuracy and stability could be improved along with enlargement of number of hidden neurons. The learning rate could not affect the prediction accuracy, and could almost affect the training speed. The training process was quite sensitive to the number of hidden neurons, even a hidden neurons change could lead to conspicuously training time prolong. It was necessary to have an applicable number of hidden neurons for predicting polygenic genotype value.
Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on; 01/2010
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ABSTRACT: Although linear multivariate approaches used to analyze large genetic data sets did not allow a large part of the total variance to be explained, strong distortions with nonlinear data sets, horseshoe effects had always been found. Artificial neural networks could gather their knowledge by detecting the patterns and relationships in data and learn through experience, and could perform well for optimization and prediction in complex non-linear systems. Artificial neural networks have been widely used in many life areas, but have not been used to predict the genomic breeding values or animal phenotypes. In this paper, Back-Propagation artificial neural network with Variable Hidden Neurons was used to predict the genomic breeding values. The results showed that artificial neural network could predict the animal genotype value, whatever there were interaction effect or not between gene loci. The sample size for training artificial neural network model could affect the training speed obviously, the training speed were obviously slowed along with enlargement of number of hidden neurons. A good structure of Back-Propagation artificial neural network needs a big sample for training its parameters. In some what, the sample size for training prediction model probably was not an important factor for prediction stability of artificial neural network; but large sample trained neural network model was very useful for training a Back-Propagation artificial neural network model with a small prediction error.
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on; 01/2010
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Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics, BMEI 2009, October 17-19, 2009, Tianjin, China; 01/2009
