A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data
Osval A. Montesinos‐López; J. Cricelio Montesinos-López; P. K. Singh; Nérida Lozano-Ramírez; Alberto Barrón-López; Abelardo Montesinos‐López; José Crossa · 2020 · G3 Genes Genomes Genetics
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Abstract
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcome
Abstract by Osval A. Montesinos‐López; J. Cricelio Montesinos-López; P. K. Singh; Nérida Lozano-Ramírez; Alberto Barrón-López; Abelardo Montesinos‐López; José Crossa, G3 Genes Genomes Genetics (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1534/g3.120.401631