ArticleJournal of animal science2018
Technical note: an R package for fitting sparse neural networks with application in animal breeding.
Article in Journal of animal science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies.G3 (Bethesda, Md.) · 2021Article
- Bayesian neural networks with variable selection for prediction of genotypic values.Genetics, selection, evolution : GSE · 2020Article
- Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri).Marine biotechnology (New York, N.Y.) · 2018Article
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Authors and funding
7 authors.
Funding
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Abstract
Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.
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