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For: Vigors S, O'Doherty JV, Bryan K, Sweeney T. A comparative analysis of the transcriptome profiles of liver and muscle tissue in pigs divergent for feed efficiency. BMC Genomics 2019;20:461. [PMID: 31170913 DOI: 10.1186/s12864-019-5740-z] [Cited by in Crossref: 12] [Cited by in F6Publishing: 17] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Davoudi P, Do DN, Colombo SM, Rathgeber B, Miar Y. Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency. Front Genet 2022;13:903733. [PMID: 35754793 DOI: 10.3389/fgene.2022.903733] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Fernández-Barroso MÁ, García-Casco JM, Núñez Y, Ramírez-Hidalgo L, Matos G, Muñoz M. Understanding the role of myoglobin content in Iberian pigs fattened in an extensive system through analysis of the transcriptome profile. Anim Genet 2022. [PMID: 35355298 DOI: 10.1111/age.13195] [Reference Citation Analysis]
3 Cai Z, Christensen OF, Lund MS, Ostersen T, Sahana G. Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans. BMC Genomics 2022;23:133. [PMID: 35168569 DOI: 10.1186/s12864-022-08373-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Kurz A, Seifert J. Factors Influencing Proteolysis and Protein Utilization in the Intestine of Pigs: A Review. Animals (Basel) 2021;11:3551. [PMID: 34944326 DOI: 10.3390/ani11123551] [Reference Citation Analysis]
5 Yang C, Han L, Li P, Ding Y, Zhu Y, Huang Z, Dan X, Shi Y, Kang X. Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq. Front Genet 2021;12:741878. [PMID: 34675965 DOI: 10.3389/fgene.2021.741878] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
6 Esmaeili M, Carter CG, Wilson R, Walker SP, Miller MR, Bridle A, Symonds JE. Proteomic investigation of liver and white muscle in efficient and inefficient Chinook salmon (Oncorhynchus tshawytscha): Fatty acid metabolism and protein turnover drive feed efficiency. Aquaculture 2021;542:736855. [DOI: 10.1016/j.aquaculture.2021.736855] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 8.0] [Reference Citation Analysis]
7 Messad F, Louveau I, Renaudeau D, Gilbert H, Gondret F. Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs. BMC Genomics 2021;22:501. [PMID: 34217223 DOI: 10.1186/s12864-021-07843-4] [Reference Citation Analysis]
8 Wang L, Zhang Y, Zhang B, Zhong H, Lu Y, Zhang H. Candidate gene screening for lipid deposition using combined transcriptomic and proteomic data from Nanyang black pigs. BMC Genomics 2021;22:441. [PMID: 34118873 DOI: 10.1186/s12864-021-07764-2] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
9 Wu J, Ye Y, Quan J, Ding R, Wang X, Zhuang Z, Zhou S, Geng Q, Xu C, Hong L, Xu Z, Zheng E, Cai G, Wu Z, Yang J. Using nontargeted LC-MS metabolomics to identify the Association of Biomarkers in pig feces with feed efficiency. Porcine Health Manag 2021;7:39. [PMID: 34078468 DOI: 10.1186/s40813-021-00219-w] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
10 Senevirathna JDM, Asakawa S. Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales. Life (Basel) 2021;11:364. [PMID: 33923876 DOI: 10.3390/life11040364] [Reference Citation Analysis]
11 Giráldez F, Santos N, Santos A, Valdés C, López S, Andrés S. Fattening lambs with divergent residual feed intakes and weight gains: Unravelling mechanisms driving feed efficiency. Animal Feed Science and Technology 2021;273:114821. [DOI: 10.1016/j.anifeedsci.2021.114821] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
12 Yang L, Wang X, He T, Xiong F, Chen X, Chen X, Jin S, Geng Z. Association of residual feed intake with growth performance, carcass traits, meat quality, and blood variables in native chickens. J Anim Sci 2020;98:skaa121. [PMID: 32303739 DOI: 10.1093/jas/skaa121] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
13 Vigors S, O' Doherty JV, Sweeney T. Colonic microbiome profiles for improved feed efficiency can be identified despite major effects of farm of origin and contemporary group in pigs. Animal 2020;14:2472-80. [PMID: 32605690 DOI: 10.1017/S1751731120001500] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
14 Nolte W, Weikard R, Brunner RM, Albrecht E, Hammon HM, Reverter A, Küehn C. Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency in Bos taurus Bulls. Int J Mol Sci 2020;21:E3292. [PMID: 32384694 DOI: 10.3390/ijms21093292] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Yang L, He T, Xiong F, Chen X, Fan X, Jin S, Geng Z. Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis. BMC Genomics 2020;21:292. [PMID: 32272881 DOI: 10.1186/s12864-020-6713-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 12] [Article Influence: 1.5] [Reference Citation Analysis]
16 de Lima AO, Koltes JE, Diniz WJS, de Oliveira PSN, Cesar ASM, Tizioto PC, Afonso J, de Souza MM, Petrini J, Rocha MIP, Cardoso TF, Neto AZ, Coutinho LL, Mourão GB, Regitano LCA. Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses. Front Genet 2020;11:189. [PMID: 32194642 DOI: 10.3389/fgene.2020.00189] [Cited by in Crossref: 5] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
17 Vigors S, O' Doherty JV, Ryan M, Sweeney T. Analysis of the basal colonic innate immune response of pigs divergent in feed efficiency and following an ex vivo lipopolysaccharide challenge. Physiol Genomics 2019;51:443-8. [PMID: 31322475 DOI: 10.1152/physiolgenomics.00013.2019] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]