2012 - Rockefeller Prentice Award in Animal Breeding and Genetics, American Society of Animal Science
While working on this project, Rohan L. Fernando studies both Artificial intelligence and Machine learning. His Machine learning study frequently links to other fields, such as Selection (genetic algorithm). As part of his studies on Selection (genetic algorithm), he frequently links adjacent subjects like Artificial intelligence. By researching both Statistics and Covariate, he produces research that crosses academic boundaries. His Marker-assisted selection research extends to the thematically linked field of Genotype. His study brings together the fields of Genotype and Marker-assisted selection. He combines topics linked to Inbred strain with his work on Genetics. His Inbred strain study frequently draws parallels with other fields, such as Gene. His research ties Linkage (software) and Gene together.
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The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values
D. Habier;R. L. Fernando;J. C. M. Dekkers.
Extension of the bayesian alphabet for genomic selection
David Habier;Rohan L Fernando;Kadir Kizilkaya;Kadir Kizilkaya;Dorian J Garrick;Dorian J Garrick.
BMC Bioinformatics (2011)
Marker assisted selection using best linear unbiased prediction
R.L. Fernando;M. Grossman.
Genetics Selection Evolution (1989)
Deregressing estimated breeding values and weighting information for genomic regression analyses
Dorian J Garrick;Dorian J Garrick;Jeremy F Taylor;Rohan L Fernando.
Genetics Selection Evolution (2009)
Additive Genetic Variability and the Bayesian Alphabet
Daniel Gianola;Daniel Gianola;Daniel Gianola;Gustavo A. de los Campos;William G. Hill;Eduardo Manfredi.
Bayesian Methods in Animal Breeding Theory
Daniel Gianola;Rohan L. Fernando.
Journal of Animal Science (1986)
Genomic-assisted prediction of genetic value with semiparametric procedures.
Daniel Gianola;Daniel Gianola;Daniel Gianola;Rohan L. Fernando;Alessandra Stella.
Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study
Shengqiang Zhong;Jack C.M. Dekkers;Rohan Luigi Fernando;Jean-Luc Jannink.
Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine ( Pinus taeda L.)
Márcio F. R. Resende;Patricio Muñoz;Marcos D. V. Resende;Marcos D. V. Resende;Dorian J. Garrick.
Genomic Selection Using Low-Density Marker Panels
David Habier;David Habier;Rohan L. Fernando;Jack C. M. Dekkers.
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