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For: 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: 13] [Cited by in F6Publishing: 17] [Article Influence: 6.5] [Reference Citation Analysis]
Number Citing Articles
1 Han G, Kim J, Kim J, Kil D. Transcriptomic analysis of the liver in aged laying hens with different eggshell strength. Poultry Science 2023;102:102217. [DOI: 10.1016/j.psj.2022.102217] [Reference Citation Analysis]
2 Su Z, Bai X, Wang H, Wang S, Chen C, Xiao F, Guo H, Gao H, Leng L, Li H. Identification of biomarkers associated with the feed efficiency by metabolomics profiling: results from the broiler lines divergent for high or low abdominal fat content. J Animal Sci Biotechnol 2022;13:122. [DOI: 10.1186/s40104-022-00775-3] [Reference Citation Analysis]
3 Jayaswamy PK, Gollapalli P, Alexander LM, M V, Patil P, Shetty P. Identification of network-based differential gene expression signatures and their transcriptional factors to develop progressive blood biomarkers for Alzheimer’s disease.. [DOI: 10.21203/rs.3.rs-2107982/v1] [Reference Citation Analysis]
4 Ramírez GA, Keshri J, Vahrson I, Garber AI, Berrang ME, Cox NA, González-cerón F, Aggrey SE, Oakley BB. Cecal Microbial Hydrogen Cycling Potential Is Linked to Feed Efficiency Phenotypes in Chickens. Front Vet Sci 2022;9:904698. [DOI: 10.3389/fvets.2022.904698] [Reference Citation Analysis]
5 Ogawa S, Darhan H, Suzuki K. Genetic and genomic analysis of oxygen consumption in mice. J Anim Breed Genet 2022. [PMID: 35608337 DOI: 10.1111/jbg.12721] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Pezeshkian Z, Mirhoseini SZ, Ghovvati S, Ebrahimie E. Transcriptome Analysis of Breast Muscle Reveals Pathways Related to Protein Deposition in High Feed Efficiency of Native Turkeys. Animals 2022;12:1240. [DOI: 10.3390/ani12101240] [Reference Citation Analysis]
7 Zhang D, Zhang X, Li F, Li X, Zhao Y, Zhang Y, Zhao L, Xu D, Wang J, Yang X, Cui P, Wang W. Identification and characterization of circular RNAs in association with the feed efficiency in Hu lambs. BMC Genomics 2022;23. [DOI: 10.1186/s12864-022-08517-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Kaewsatuan P, Poompramun C, Kubota S, Yongsawatdigul J, Molee W, Uimari P, Molee A. Comparative proteomics revealed duodenal metabolic function associated with feed efficiency in slow-growing chicken. Poultry Science 2022. [DOI: 10.1016/j.psj.2022.101824] [Reference Citation Analysis]
9 Karimi P, Bakhtiarizadeh MR, Salehi A, Izadnia HR. Transcriptome analysis reveals the potential roles of long non-coding RNAs in feed efficiency of chicken. Sci Rep 2022;12:2558. [PMID: 35169237 DOI: 10.1038/s41598-022-06528-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 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 Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
11 Poompramun C, Hennequet-Antier C, Thumanu K, Sinpru P, Pengsanthia S, Molee W, Molee A, Le Bihan-Duval E, Juanchich A. Revealing Pathways Associated with Feed Efficiency and Meat Quality Traits in Slow-Growing Chickens. Animals (Basel) 2021;11:2977. [PMID: 34679997 DOI: 10.3390/ani11102977] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Sinpru P, Riou C, Kubota S, Poompramun C, Molee W, Molee A. Jejunal Transcriptomic Profiling for Differences in Feed Conversion Ratio in Slow-Growing Chickens. Animals (Basel) 2021;11:2606. [PMID: 34573572 DOI: 10.3390/ani11092606] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Ahmad S, Drag MH, Salleh SM, Cai Z, Nielsen MO. Transcriptomics analysis of differentially expressed genes in subcutaneous and perirenal adipose tissue of sheep as affected by their pre- and early postnatal malnutrition histories. BMC Genomics 2021;22:338. [PMID: 33975549 DOI: 10.1186/s12864-021-07672-5] [Reference Citation Analysis]
14 Chen W, Alexandre PA, Ribeiro G, Fukumasu H, Sun W, Reverter A, Li Y. Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi-Tissue Transcriptome Data. Front Genet 2021;12:619857. [PMID: 33664767 DOI: 10.3389/fgene.2021.619857] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
15 Han GP, Kim JM, Kang HK, Kil DY. Transcriptomic analysis of the liver in aged laying hens with different intensity of brown eggshell color. Anim Biosci 2021;34:811-23. [PMID: 33152221 DOI: 10.5713/ajas.20.0237] [Reference Citation Analysis]