Guilherme J M Rosa

An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.

Chuanyu Sun, Xiao-Lin Wu, Kent A Weigel, 2012, Genetics research.

Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.

José Crossa, Kent A Weigel, Guilherme J M Rosa, 2010, Genetics research.

Technical note: an R package for fitting sparse neural networks with application in animal breeding.

Zhihui Chen, Guilherme J M Rosa, Yangfan Wang, 2018, Journal of animal science.

Searching for Recursive Causal Structures in Multivariate Quantitative Genetics Mixed Models

Guilherme J M Rosa, Daniel Gianola, Bruno D Valente, 2010, Genetics.

Conceptual framework for investigating causal effects from observational data in livestock.

Guilherme J M Rosa, Daniel Gianola, Nora M Bello, 2018, Journal of animal science.

Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes.

Guilherme J M Rosa, Bruno D Valente, Martinho A Silva, 2014, Poultry science.

A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data.

Guilherme J M Rosa, Rosangela Poletto, J. Steibel, 2009, Genomics.