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Integrating-nir-genomic-kernel/
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Bem-vindo! Este repositório reúne código, dados e relatórios
associados ao estudo da integração de espectroscopia no
infravermelho próximo (NIR) e dados genômicos para aprimorar a predição
fenômica, utilizando métodos de kernel para capturar relações
não lineares e interações complexas entre genótipo, ambiente e
espectro.
O objetivo é avaliar se a fusão dessas fontes de dados pode aumentar a
acurácia preditiva e apoiar programas de melhoramento genético mais
eficientes.
Este trabalho faz parte do Projeto de Pesquisa:
Este projeto foi desenvolvido no âmbito das pesquisas do Laboratório de Inteligência Computacional e Aprendizado Estatístico (LICAE) da Universidade Federal de Viçosa (UFV), especializado em aplicações avançadas de inteligência computacional e modelagem estatística em problemas genômicos complexos.
Contribuições são bem-vindas mediante:
- Abertura de issues para discussão de melhorias
- Submissão de pull requests para correções críticas
- Sugestões de extensões metodológicas
Este trabalho está licenciado sob CC BY-NC-SA
4.0.
Para uso comercial ou modificações significativas, contate os
autores.
Coordenador
Moyses Nascimento
Professor Adjunto - Departamento de Estatística - UFV
moysesnascim@ufv.br
Bolsista
Weverton Gomes da Costa
Pós-Doutorando - Departamento de Estatística - UFV
weverton.costa@ufv.br
Laboratório LICAE:
licae@ufv.br | https://www.licae.ufv.br/
Welcome! This repository contains code, data, and reports related to
the study of integrating near-infrared reflectance spectroscopy
(NIR) and genomic data to improve phenomic prediction, using
kernel methods to capture non-linear relationships and complex genotype
× environment × spectrum interactions.
The goal is to assess whether merging these data sources can increase
predictive accuracy and support more efficient breeding programs.
This work is part of the Research Project:
This project was developed within the research activities of the Computational Intelligence and Statistical Learning Laboratory (LICAE) at the Federal University of Viçosa (UFV), specialized in advanced computational intelligence and statistical modeling for complex genomic problems.
Contributions are welcome via:
- Opening issues for improvement discussions
- Submitting pull requests for critical fixes
- Suggesting methodological extensions
This work is licensed under CC BY-NC-SA
4.0.
For commercial use or significant modifications, please contact the
authors.
Project Coordinator
Moyses Nascimento
Associate Professor – Department of Statistics – UFV
moysesnascim@ufv.br
Research Fellow
Weverton Gomes da Costa
Post-Doctoral Researcher – Department of Statistics – UFV
weverton.costa@ufv.br
LICAE Laboratory:
licae@ufv.br | https://www.licae.ufv.br/
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
[3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.utf8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 cli_3.6.5 knitr_1.50 rlang_1.1.6
[5] xfun_0.53 stringi_1.8.7 promises_1.3.3 jsonlite_2.0.0
[9] workflowr_1.7.2 glue_1.8.0 rprojroot_2.1.1 git2r_0.36.2
[13] htmltools_0.5.8.1 httpuv_1.6.16 sass_0.4.10 rmarkdown_2.29
[17] evaluate_1.0.5 jquerylib_0.1.4 tibble_3.3.0 fastmap_1.2.0
[21] yaml_2.3.10 lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.2
[25] compiler_4.5.1 fs_1.6.6 Rcpp_1.1.0 pkgconfig_2.0.3
[29] rstudioapi_0.17.1 later_1.4.4 digest_0.6.37 R6_2.6.1
[33] pillar_1.11.1 magrittr_2.0.4 bslib_0.9.0 tools_4.5.1
[37] cachem_1.1.0 Weverton Gomes da Costa, Pós-Doutorando, Departamento de Estatística - Universidade Federal de Viçosa, wevertonufv@gmail.com↩︎