The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanie...
We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method speci...
Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of ge...
Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of gen...
BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that o...
In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how m...
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative...
Proceedings of the National Academy of Sciences of the United States of America
Mar 2, 2021
Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interact...
Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to...
Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
Nov 1, 2015
This paper introduces a method called model-based multifactor dimensionality reduction (MB-MDR), which was firstly proposed by Calle et al., and can be applied for detecting gene-gene or gene-environment interactions in genetic studies. The basic pri...
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