249 related articles for article (PubMed ID: 30866799)
1. Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs.
Piles M; Fernandez-Lozano C; Velasco-Galilea M; González-Rodríguez O; Sánchez JP; Torrallardona D; Ballester M; Quintanilla R
Genet Sel Evol; 2019 Mar; 51(1):10. PubMed ID: 30866799
[TBL] [Abstract][Full Text] [Related]
2. Integrative approach using liver and duodenum RNA-Seq data identifies candidate genes and pathways associated with feed efficiency in pigs.
Ramayo-Caldas Y; Ballester M; Sánchez JP; González-Rodríguez O; Revilla M; Reyer H; Wimmers K; Torrallardona D; Quintanilla R
Sci Rep; 2018 Jan; 8(1):558. PubMed ID: 29323241
[TBL] [Abstract][Full Text] [Related]
3. A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs.
Gondret F; Vincent A; Houée-Bigot M; Siegel A; Lagarrigue S; Causeur D; Gilbert H; Louveau I
BMC Genomics; 2017 Mar; 18(1):244. PubMed ID: 28327084
[TBL] [Abstract][Full Text] [Related]
4. Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.
Mora M; González P; Quevedo JR; Montañés E; Tusell L; Bergsma R; Piles M
J Anim Breed Genet; 2023 Nov; 140(6):638-652. PubMed ID: 37403756
[TBL] [Abstract][Full Text] [Related]
5. Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs.
Messad F; Louveau I; Renaudeau D; Gilbert H; Gondret F
BMC Genomics; 2021 Jul; 22(1):501. PubMed ID: 34217223
[TBL] [Abstract][Full Text] [Related]
6. RNA-Seq transcriptomics and pathway analyses reveal potential regulatory genes and molecular mechanisms in high- and low-residual feed intake in Nordic dairy cattle.
Salleh MS; Mazzoni G; Höglund JK; Olijhoek DW; Lund P; Løvendahl P; Kadarmideen HN
BMC Genomics; 2017 Mar; 18(1):258. PubMed ID: 28340555
[TBL] [Abstract][Full Text] [Related]
7. Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs.
Messad F; Louveau I; Koffi B; Gilbert H; Gondret F
BMC Genomics; 2019 Aug; 20(1):659. PubMed ID: 31419934
[TBL] [Abstract][Full Text] [Related]
8. Post-weaning blood transcriptomic differences between Yorkshire pigs divergently selected for residual feed intake.
Liu H; Nguyen YT; Nettleton D; Dekkers JC; Tuggle CK
BMC Genomics; 2016 Jan; 17():73. PubMed ID: 26801403
[TBL] [Abstract][Full Text] [Related]
9. Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs.
Ramayo-Caldas Y; Mármol-Sánchez E; Ballester M; Sánchez JP; González-Prendes R; Amills M; Quintanilla R
Genet Sel Evol; 2019 Sep; 51(1):48. PubMed ID: 31477014
[TBL] [Abstract][Full Text] [Related]
10. RNA-seq of muscle from pigs divergent in feed efficiency and product quality identifies differences in immune response, growth, and macronutrient and connective tissue metabolism.
Horodyska J; Wimmers K; Reyer H; Trakooljul N; Mullen AM; Lawlor PG; Hamill RM
BMC Genomics; 2018 Nov; 19(1):791. PubMed ID: 30384851
[TBL] [Abstract][Full Text] [Related]
11. Increased expressions of genes and proteins involved in mitochondrial oxidation and antioxidant pathway in adipose tissue of pigs selected for a low residual feed intake.
Louveau I; Vincent A; Tacher S; Gilbert H; Gondret F
J Anim Sci; 2016 Dec; 94(12):5042-5054. PubMed ID: 28046150
[TBL] [Abstract][Full Text] [Related]
12. Selection response and genetic parameters for residual feed intake in Yorkshire swine.
Cai W; Casey DS; Dekkers JC
J Anim Sci; 2008 Feb; 86(2):287-98. PubMed ID: 17998435
[TBL] [Abstract][Full Text] [Related]
13. Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle.
Yao C; Spurlock DM; Armentano LE; Page CD; VandeHaar MJ; Bickhart DM; Weigel KA
J Dairy Sci; 2013 Oct; 96(10):6716-29. PubMed ID: 23932129
[TBL] [Abstract][Full Text] [Related]
14. Phenotypic and genetic relationships between growth and feed intake curves and feed efficiency and amino acid requirements in the growing pig.
Saintilan R; Brossard L; Vautier B; Sellier P; Bidanel J; van Milgen J; Gilbert H
Animal; 2015 Jan; 9(1):18-27. PubMed ID: 25192352
[TBL] [Abstract][Full Text] [Related]
15. Divergent selection for feed efficiency in pigs altered the duodenum transcriptomic response to feed intake and its DNA methylation profiles.
Devailly G; Fève K; Saci S; Sarry J; Valière S; Lluch J; Bouchez O; Ravon L; Billon Y; Gilbert H; Riquet J; Beaumont M; Demars J
Physiol Genomics; 2024 May; 56(5):397-408. PubMed ID: 38497119
[TBL] [Abstract][Full Text] [Related]
16. Transcriptomic analysis to elucidate the molecular mechanisms that underlie feed efficiency in meat-type chickens.
Lee J; Karnuah AB; Rekaya R; Anthony NB; Aggrey SE
Mol Genet Genomics; 2015 Oct; 290(5):1673-82. PubMed ID: 25782841
[TBL] [Abstract][Full Text] [Related]
17. Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake.
Kong RS; Liang G; Chen Y; Stothard P; Guan le L
BMC Genomics; 2016 Aug; 17():592. PubMed ID: 27506548
[TBL] [Abstract][Full Text] [Related]
18. Acute systemic inflammatory response to lipopolysaccharide stimulation in pigs divergently selected for residual feed intake.
Liu H; Feye KM; Nguyen YT; Rakhshandeh A; Loving CL; Dekkers JCM; Gabler NK; Tuggle CK
BMC Genomics; 2019 Oct; 20(1):728. PubMed ID: 31610780
[TBL] [Abstract][Full Text] [Related]
19. Transcriptome Analysis of Skeletal Muscle in Pigs with Divergent Residual Feed Intake Phenotypes.
Hou X; Pu L; Wang L; Liu X; Gao H; Yan H; Zhang J; Zhang Y; Yue J; Zhang L; Wang L
DNA Cell Biol; 2020 Mar; 39(3):404-416. PubMed ID: 32004088
[TBL] [Abstract][Full Text] [Related]
20. Divergent selection for residual feed intake affects the transcriptomic and proteomic profiles of pig skeletal muscle.
Vincent A; Louveau I; Gondret F; Tréfeu C; Gilbert H; Lefaucheur L
J Anim Sci; 2015 Jun; 93(6):2745-58. PubMed ID: 26115262
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
