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Your search keyword '"Carvalho, Humberto Fanelli"' showing total 25 results
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25 results on '"Carvalho, Humberto Fanelli"'

7. Quantitative trait loci related to growth and wood quality traits in Eucalyptus grandis W. Hill identified through single- and multi-trait genome-wide association studies

Author

Rocha, Lucas Fernandes, Benatti, Thiago Romanos, de Siqueira, Leandro, de Souza, Izabel Christina Gava, Bianchin, Isadora, de Souza, Aguinaldo José, Fernandes, Aline Cristina Miranda, Oda, Shinitiro, Stape, José Luiz, Yassue, Rafael Massahiro, Carvalho, Humberto Fanelli, Müller, Niels A., Fladung, Matthias, Acosta, Juan Jose, Fritsche-Neto, Roberto, and Tambarussi, Evandro Vagner

Published
2022
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12. Automated Machine Learning: A Case Study of Genomic 'Image-Based' Prediction in Maize Hybrids

Author

Galli, Giovanni, Sabadin, Felipe, Yassue, Rafael Massahiro, Galves, Cassia, Carvalho, Humberto Fanelli, Crossa, Jose, Montesinos-Lopez, Osval Antonio, Fritsche-Neto, Roberto, Galli, Giovanni, Sabadin, Felipe, Yassue, Rafael Massahiro, Galves, Cassia, Carvalho, Humberto Fanelli, Crossa, Jose, Montesinos-Lopez, Osval Antonio, and Fritsche-Neto, Roberto

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 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.

Published
2022

25. EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture.

Author

Costa-Neto G, Galli G, Carvalho HF, Crossa J, and Fritsche-Neto R

Subjects
Agriculture, Genomics, Genotype, Phenotype, Software, Gene-Environment Interaction, Models, Genetic
Abstract

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Published
2021
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