Zobrazeno 1 - 10
of 18
pro vyhledávání: '"José Cricelio Montesinos-López"'
Autor:
Osval Antonio Montesinos‐López, Henry Nicole Gonzalez, Abelardo Montesinos‐López, María Daza‐Torres, Morten Lillemo, José Cricelio Montesinos‐López, José Crossa
Publikováno v:
The Plant Genome, Vol 15, Iss 3, Pp n/a-n/a (2022)
Abstract Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine‐learning model using available phenotypic and genotypic data with which predictions are performed for indivi
Externí odkaz:
https://doaj.org/article/b171321aeea04ec4928e6a4a54b5a5cb
Autor:
Osval Antonio Montesinos-López, José Cricelio Montesinos-López, Pawan Singh, Nerida Lozano-Ramirez, Alberto Barrón-López, Abelardo Montesinos-López, José Crossa
Publikováno v:
G3: Genes, Genomes, Genetics, Vol 10, Iss 11, Pp 4177-4190 (2020)
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used
Externí odkaz:
https://doaj.org/article/9973a996e43c4834a37fa9c27c261107
Autor:
José Cricelio Montesinos-López, Maria L. Daza-Torres, Yury E. García, Luis A. Barboza, Fabio Sanchez, Alec J. Schmidt, Brad H. Pollock, Miriam Nuño
Publikováno v:
Life, Vol 11, Iss 12, p 1336 (2021)
The rapid spread of the new SARS-CoV-2 virus triggered a global health crisis, disproportionately impacting people with pre-existing health conditions and particular demographic and socioeconomic characteristics. One of the main concerns of governmen
Externí odkaz:
https://doaj.org/article/317a2669f87d45bcb2b6ceb648e97fdb
Autor:
Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, José Cricelio Montesinos-López, Francisco Javier Luna-Vázquez, Josafhat Salinas-Ruiz, José R. Herrera-Morales, Raymundo Buenrostro-Mariscal
Publikováno v:
G3: Genes, Genomes, Genetics, Vol 7, Iss 6, Pp 1833-1853 (2017)
There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits
Externí odkaz:
https://doaj.org/article/1cb22c4dd84c46a1a9c545bbb4e24ec4
Autor:
Osval A. Montesinos-López, José Crossa, Abelardo Montesinos-López, Roberto de la Rosa, José Cricelio Montesinos-López, Carlos Flores-Cortés
Publikováno v:
Osval A. Montesinos-López
The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63f9d67cb3903544f508c008d7604542
https://doi.org/10.1038/s41437-021-00412-1
https://doi.org/10.1038/s41437-021-00412-1
Autor:
Osval Antonio, Montesinos-López, Abelardo, Montesinos-López, Brandon A, Mosqueda-Gonzalez, José Cricelio, Montesinos-López, José, Crossa
Publikováno v:
Methods in molecular biology (Clifton, N.J.). 2467
Genomic enabled prediction is playing a key role for the success of genomic selection (GS). However, according to the No Free Lunch Theorem, there is not a universal model that performs well for all data sets. Due to this, many statistical and machin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::32c5e788b39b598aa81234ca66d08126
https://pubmed.ncbi.nlm.nih.gov/35451780
https://pubmed.ncbi.nlm.nih.gov/35451780
Autor:
Alberto Barrón-López, José Crossa, Pawan K. Singh, Nerida Lozano-Ramirez, José Cricelio Montesinos-López, Osval A. Montesinos-López, Abelardo Montesinos-López
Publikováno v:
G3: Genes, Genomes, Genetics, Vol 10, Iss 11, Pp 4177-4190 (2020)
G3: Genes|Genomes|Genetics
G3: Genes|Genomes|Genetics
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c0d63f6b7d738e6300dcae4c87ca3c0
https://doi.org/10.1534/g3.120.401631
https://doi.org/10.1534/g3.120.401631
Autor:
Osval Antonio Montesinos-López, Abelardo Montesinos-López, Brandon A. Mosqueda-Gonzalez, José Cricelio Montesinos-López, José Crossa
Publikováno v:
Methods in Molecular Biology ISBN: 9781071622049
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9598110ba5db27e869472c2ffce13d6a
https://doi.org/10.1007/978-1-0716-2205-6_10
https://doi.org/10.1007/978-1-0716-2205-6_10
Autor:
Raymundo Buenrostro-Mariscal, Abelardo Montesinos-López, Eduardo Salazar, José Cricelio Montesinos-López, José Crossa, Osval A. Montesinos-López, Jose Alberto Barron
Publikováno v:
The Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous ou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33775514cb251741593c7707dfa1ec72
https://pubmed.ncbi.nlm.nih.gov/34323393
https://pubmed.ncbi.nlm.nih.gov/34323393
Autor:
Brandon A Mosqueda-Gonzalez, Felícitas Alejandra Valladares-Anguiano, Pawan K. Singh, Nerida Lozano Ramirez, Abelardo Montesinos-López, José Cricelio Montesinos-López, José Crossa, Osval A. Montesinos-López
Publikováno v:
G3: Genes|Genomes|Genetics
In genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c0f61e2e275ece2b6e27e8453125236
https://doi.org/10.1093/g3journal/jkaa057
https://doi.org/10.1093/g3journal/jkaa057