Author

Erivaldo José Scaloppi

ORCID 0000-0002-8050-2187Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:genome assembly· 1 Organism:multiple species· 1 Organism:plant (other)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Anete Pereira de SouzaiD· 2 shared Felipe Roberto FranciscoiD· 2 shared Livia Moura SouzaiD· 2 shared Roberto Fritsche‐NetoiD· 2 shared Vincent Le GueniD· 2 shared Alexandre Hild AonoiD· 1 shared Gregor GorjanciD· 1 shared Marcos G. QuilesiD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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2 shared papersGenetics

Anete Pereira de SouzaORCID

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2 papers

Recent·Most cited

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Alexandre Hild Aono, Felipe Roberto Francisco, Livia Moura Souza, Paulo de Souza Gonçalves, Erivaldo José Scaloppi, Vincent Le Guen, Roberto Fritsche‐Neto, Gregor Gorjanc, Marcos G. Quiles, Anete Pereira de Souza

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

Scientific Reports · Oct 26, 2022Peer-reviewedGenetics

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G x E

Livia Moura Souza, Felipe Roberto Francisco, P.S. Gonçalves, Erivaldo José Scaloppi, Vincent Le Guen, Roberto Fritsche‐Neto, Anete Pereira de Souza

This study compares genomic selection models for rubber trees, finding that multi-environment models with nonlinear and linear kernels improve prediction accuracy and genetic gain compared to single-environment models.

Frontiers in Plant Science · Oct 25, 2019Peer-reviewedGenetics