Technical note: an R package for fitting sparse neural networks with application in animal breeding1

• Published in: Journal of animal science Vol. 96; no. 5; pp. 2016 - 2026
• Main Authors: Wang, Yangfan, Mi, Xue, Rosa, Guilherme J M, Chen, Zhihui, Lin, Ping, Wang, Shi, Bao, Zhenmin
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 04.05.2018
• Subjects: Technology in Animal Science; animal breeding; genomic selection; dominance and additive effects; genetic markers; sparse neural networks
• ISSN: 0021-8812; 1525-3163
• DOI: 10.1093/jas/sky071

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.