Author

Lucimara Chiari

EmbrapaORCID 0000-0003-3030-7532Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Alexandre Hild AonoiD· 2 shared Aline da Costa Lima MoraesiD· 2 shared Anete Pereira de SouzaiD· 2 shared Cacilda Borges do ValleiD· 2 shared Liana JankiD· 2 shared Mateus Figueirêdo SantosiD· 2 shared Rebecca Caroline Ulbricht FerreiraiD· 2 shared Sanzio Carvalho Lima BarriosiD· 2 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Alexandre Hild AonoORCID

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2 papers

Recent·Most cited

A joint learning approach for genomic prediction in polyploid grasses

Alexandre Hild Aono, Rebecca Caroline Ulbricht Ferreira, Aline da Costa Lima Moraes, Letícia Aparecida de Castro Lara, Ricardo José Gonzaga Pimenta, Estela Araújo Costa, Luciana Rossini Pinto, Marcos Guimarães de Andrade Landell, Mateus Figueirêdo Santos, Liana Jank, Sanzio Carvalho Lima Barrios, Cacilda Borges do Valle, Lucimara Chiari, Antônio Augusto Franco Garcia, Reginaldo Massanobu Kuroshu, , Anete Pereira de Souza

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

Scientific Reports · Jul 21, 2022Peer-reviewedBioinformatics

An Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

Felipe Bitencourt Martins, Aline da Costa Lima Moraes, Alexandre Hild Aono, Rebecca Caroline Ulbricht Ferreira, Lucimara Chiari, Rosangela Maria Simeão, Sanzio Carvalho Lima Barrios, Mateus Figueirêdo Santos, Liana Jank, Cacilda Borges do Valle, B. B. Z. Vigna, Anete Pereira de Souza

This paper introduces an automated SNP-based method to identify contaminants in polyploid plant progenies using PCA, Mendelian segregation analysis, and clustering, validated on simulated and real data.

Frontiers in Plant Science · Oct 22, 2021Peer-reviewedGenomics