Automated machine learning: a case study of genomic 'image-based' prediction in maize hybrids

Autor: Galli, Giovanni, Sabadin, Felipe, Yassue, Rafael Massahiro, Galves, Cassia, Carvalho, Humberto Fanelli, Crossa, Jose, Montesinos-Lopez, Osval Antonio, Fritsche-Neto, Roberto
Rok vydání: 2022
Předmět:
Zdroj: Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Popis: 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 for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil (CAPES) [001]; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); Bill and Melinda Gates Foundation (BMGF) [INV-003439 BMGF/FCDO] Published version This work was financially supported by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior Brasil (CAPES) - Finance Code 001 and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq). Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) and the Bill and Melinda Gates Foundation (BMGF): Grant Number INV-003439 BMGF/FCDO for the financial support. Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW).
Databáze: OpenAIRE
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