2016 - Rockefeller Prentice Award in Animal Breeding and Genetics, American Society of Animal Science
Guilherme J. M. Rosa mostly deals with Genetics, Statistics, Linear model, Single-nucleotide polymorphism and SNP. His Genetics study integrates concerns from other disciplines, such as Bayesian linear regression and Bayesian probability, Bayes' theorem. As a part of the same scientific family, Guilherme J. M. Rosa mostly works in the field of Bayesian linear regression, focusing on Regression analysis and, on occasion, Artificial intelligence.
Guilherme J. M. Rosa focuses mostly in the field of Statistics, narrowing it down to topics relating to Selection and, in certain cases, Genetic model. His study focuses on the intersection of Linear model and fields such as Mixed model with connections in the field of Generalized linear mixed model, Bioinformatics, Goodness of fit and Recursion. The study incorporates disciplines such as Allele, Embryo and Human fertilization in addition to Single-nucleotide polymorphism.
His primary areas of investigation include Statistics, Genetics, Animal science, Heritability and Selection. Statistics and Residual are frequently intertwined in his study. His Single-nucleotide polymorphism, Quantitative trait locus, Genotype, Genome-wide association study and Gene study are his primary interests in Genetics.
His Animal science study incorporates themes from Ice calving and Lactation. His work on Genetic correlation expands to the thematically related Heritability. His research on Linear model frequently links to adjacent areas such as Mixed model.
Guilherme J. M. Rosa spends much of his time researching Animal science, Statistics, Artificial intelligence, Beef cattle and Structural equation modeling. Guilherme J. M. Rosa has researched Animal science in several fields, including Weight gain, Metritis, Birth weight and Selection. His Statistics research is multidisciplinary, incorporating elements of Purebred, Tick infestation and Heritability.
His Artificial intelligence research includes themes of Mean squared error, Machine learning, Computer vision and Data analysis. His research in Beef cattle intersects with topics in Feedlot and Linear regression. Guilherme J. M. Rosa combines subjects such as Regression analysis and Regression with his study of Bayes' theorem.
Guilherme J. M. Rosa mainly investigates Artificial intelligence, Genome-wide association study, SNP, Genetics and Animal science. The various areas that Guilherme J. M. Rosa examines in his Artificial intelligence study include Machine learning and Computer vision. His study in Genome-wide association study is interdisciplinary in nature, drawing from both Dairy cattle, Brown Swiss, Udder, Structural equation modeling and Genetic architecture.
His research integrates issues of Regression, Computational biology, Bayesian probability and DNA methylation in his study of SNP. Guilherme J. M. Rosa works in the field of Genetics, focusing on Candidate gene in particular. His Animal science research integrates issues from Fertility, Metritis, Ice calving and Artificial insemination.
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Synchronization rate, size of the ovulatory follicle, and pregnancy rate after synchronization of ovulation beginning on different days of the estrous cycle in lactating dairy cows.
J.L.M. Vasconcelos;J.L.M. Vasconcelos;R.W. Silcox;G.J.M. Rosa;J.R. Pursley.
Comparison of Ovarian Function and Circulating Steroids in Estrous Cycles of Holstein Heifers and Lactating Cows
R. Sartori;J.M. Haughian;R.D. Shaver;G.J.M. Rosa.
Journal of Dairy Science (2004)
Glucosamine and chondroitin sulfate regulate gene expression and synthesis of nitric oxide and prostaglandin E2 in articular cartilage explants
P. S. Chan;J. P. Caron;G. J M Rosa;Michael W. Orth.
Osteoarthritis and Cartilage (2005)
Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods
Gustavo De Los Campos;Daniel Gianola;Guilherme J. M. Rosa;Kent A. Weigel.
Genetics Research (2010)
Ovarian Structures and Circulating Steroids in Heifers and Lactating Cows in Summer and Lactating and Dry Cows in Winter
R. Sartori;G.J.M. Rosa;M.C. Wiltbank.
Journal of Dairy Science (2002)
The transcriptome of human oocytes
Arif Murat Kocabas;Javier Crosby;Pablo J. Ross;Hasan H. Otu;Hasan H. Otu.
Proceedings of the National Academy of Sciences of the United States of America (2006)
A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data
Juan Pedro Steibel;Rosangela Poletto;Paul M. Coussens;Guilherme J.M. Rosa.
Mutations in the STAT5A gene are associated with embryonic survival and milk composition in cattle.
H. Khatib;R.L. Monson;V. Schutzkus;D.M. Kohl.
Journal of Dairy Science (2008)
Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat
Daniel Gianola;Hayrettin Okut;Kent A Weigel;Guilherme Jm Rosa.
BMC Genetics (2011)
Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation
G.J.M. Rosa;C.R. Padovani;D. Gianola.
Biometrical Journal (2003)
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