Automated Machine Learning: A Case Study of Genomic "Image-Based" Prediction in Maize Hybrids.

ABSTRACT: Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as "genomic images." In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search

SUBMITTER: Galli G 

PROVIDER: S-EPMC8936805 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature