Deep learning genomic-enabled prediction of plant traits

Abstract

Machine learning (ML) is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) from data, without being explicitly programmed to do this. ML is closely related to (and often overlaps with) computational statistics, which also focuses on making predictions through the use of computers. In general, ML explores algorithms that can learn from current data and make predictions on new data, through building a model from sample inputs. The field of statistics and ML had a root in common and will continue to come closer together in the future. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. DL models with densely connected network architecture were compared with one of the most often used genome-enabled prediction models genomic best linear unbiased prediction (GBLUP). We used nine published real genomic data sets to compare the models and obtain a “meta picture” of the performance of DL models with a densely connected network architecture.