M. S. Mayes

1.1k total citations
26 papers, 843 citations indexed

About

M. S. Mayes is a scholar working on Animal Science and Zoology, Genetics and Agronomy and Crop Science. According to data from OpenAlex, M. S. Mayes has authored 26 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Animal Science and Zoology, 11 papers in Genetics and 6 papers in Agronomy and Crop Science. Recurrent topics in M. S. Mayes's work include Genetic and phenotypic traits in livestock (9 papers), Meat and Animal Product Quality (7 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). M. S. Mayes is often cited by papers focused on Genetic and phenotypic traits in livestock (9 papers), Meat and Animal Product Quality (7 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). M. S. Mayes collaborates with scholars based in United States, New Zealand and France. M. S. Mayes's co-authors include Steven M. Lonergan, L. J. Rowe, E. Huff‐Lonergan, Ted W. Huiatt, Richard G. Tait, James M. Reecy, Marvin H. Stromer, Dorian J. Garrick, Jin Ah Cho and Eun‐Hwan Jeong and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and American Journal of Obstetrics and Gynecology.

In The Last Decade

M. S. Mayes

25 papers receiving 811 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. S. Mayes United States 13 462 237 190 167 119 26 843
G. S. Nattrass Australia 15 367 0.8× 256 1.1× 89 0.5× 193 1.2× 100 0.8× 28 775
Deping Han China 18 193 0.4× 246 1.0× 39 0.2× 130 0.8× 52 0.4× 45 781
J. Tibau Spain 19 655 1.4× 184 0.8× 24 0.1× 322 1.9× 105 0.9× 53 1.2k
Anneleen Stinckens Belgium 16 265 0.6× 260 1.1× 45 0.2× 269 1.6× 110 0.9× 35 679
Xianbo Jia China 17 254 0.5× 459 1.9× 42 0.2× 164 1.0× 167 1.4× 101 952
C. Ojano-Dirain United States 18 377 0.8× 242 1.0× 24 0.1× 132 0.8× 169 1.4× 32 849
Hua Yan China 17 397 0.9× 285 1.2× 44 0.2× 327 2.0× 83 0.7× 47 938
Lusheng Huang China 14 229 0.5× 417 1.8× 56 0.3× 248 1.5× 102 0.9× 33 789
Caroline Molette France 16 526 1.1× 303 1.3× 54 0.3× 97 0.6× 80 0.7× 48 1.0k
Stephen L. Gaffin United States 11 153 0.3× 341 1.4× 48 0.3× 75 0.4× 197 1.7× 18 797

Countries citing papers authored by M. S. Mayes

Since Specialization
Citations

This map shows the geographic impact of M. S. Mayes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. S. Mayes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. S. Mayes more than expected).

Fields of papers citing papers by M. S. Mayes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. S. Mayes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. S. Mayes. The network helps show where M. S. Mayes may publish in the future.

Co-authorship network of co-authors of M. S. Mayes

This figure shows the co-authorship network connecting the top 25 collaborators of M. S. Mayes. A scholar is included among the top collaborators of M. S. Mayes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. S. Mayes. M. S. Mayes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mayes, M. S., et al.. (2024). The genetic architecture of complete blood counts in lactating Holstein dairy cows. Frontiers in Genetics. 15. 1360295–1360295. 1 indexed citations
2.
Mayes, M. S., et al.. (2024). Association of milk metabolites with feed intake and traits impacting feed efficiency in lactating Holstein dairy cows. SHILAP Revista de lepidopterología. 5. 1 indexed citations
3.
Mayes, M. S., Patrick J. Gorden, Dawn Koltes, et al.. (2023). The impact of health disorders on automated sensor measures and feed intake in lactating Holstein dairy cattle. SHILAP Revista de lepidopterología. 3. 6 indexed citations
4.
Kramer, Luke, M. S. Mayes, Richard G. Tait, et al.. (2019). Genome-wide association study for response to vaccination in Angus calves1. BMC Genetics. 20(1). 6–6. 4 indexed citations
5.
6.
Kramer, Luke, M. S. Mayes, Eric Fritz-Waters, et al.. (2017). Evaluation of responses to vaccination of Angus cattle for four viruses that contribute to bovine respiratory disease complex1,2. Journal of Animal Science. 95(11). 4820–4834. 5 indexed citations
7.
Kramer, Luke, James E. Koltes, Eric Fritz-Waters, et al.. (2016). Epistatic interactions associated with fatty acid concentrations of beef from angus sired beef cattle. BMC Genomics. 17(1). 891–891. 7 indexed citations
8.
Cheng, Ye, Ángela Cánovas, M. S. Mayes, et al.. (2013). Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects. BMC Genetics. 14(1). 103–103. 4 indexed citations
9.
Saatchi, Mahdi, Dorian J. Garrick, Richard G. Tait, et al.. (2013). Genome-wide association and prediction of direct genomic breeding values for composition of fatty acids in Angus beef cattlea. BMC Genomics. 14(1). 730–730. 61 indexed citations
10.
Mateescu, Raluca G., Dorian J. Garrick, Richard G. Tait, et al.. (2013). Genome-wide association study of concentrations of iron and other minerals in longissimus muscle of Angus cattle1. Journal of Animal Science. 91(8). 3593–3600. 12 indexed citations
11.
Mateescu, Raluca G., A. J. Garmyn, Richard G. Tait, et al.. (2012). Genetic parameters for carnitine, creatine, creatinine, carnosine, and anserine concentration in longissimus muscle and their association with palatability traits in Angus cattle1. Journal of Animal Science. 90(12). 4248–4255. 33 indexed citations
12.
13.
Cheng, Ye, et al.. (2011). Mapping genetic loci that interact with myostatin to affect growth traits. Heredity. 107(6). 565–573. 5 indexed citations
14.
Garmyn, A. J., G. G. Hilton, Raluca G. Mateescu, et al.. (2011). Estimation of relationships between mineral concentration and fatty acid composition of longissimus muscle and beef palatability traits1. Journal of Animal Science. 89(9). 2849–2858. 76 indexed citations
15.
Lonergan, Steven M., et al.. (2004). Early postmortem biochemical factors influence tenderness and water-holding capacity of three porcine muscles1. Journal of Animal Science. 82(4). 1195–1205. 317 indexed citations
16.
Lonergan, Steven M., et al.. (2004). Early postmortem biochemical factors influence tenderness and water-holding capacity of three porcine muscles1. Journal of Animal Science. 82(4). 1195–1205. 12 indexed citations
17.
Stromer, Marvin H., M. S. Mayes, & Robert M. Bellin. (2002). Use of actin isoform-specific antibodies to probe the domain structure in three smooth muscles. Histochemistry and Cell Biology. 118(4). 291–299. 9 indexed citations
18.
Krämer, W, et al.. (1999). A randomized double-blind study comparing the fetal effects of sulindac to terbutaline during the management of preterm labor. American Journal of Obstetrics and Gynecology. 180(2). 396–401. 31 indexed citations
19.
Moise, Kenneth J., et al.. (1995). An animal model for hemolytic disease of the fetus and newborn. American Journal of Obstetrics and Gynecology. 173(3). 747–753. 10 indexed citations
20.
Krämer, W, et al.. (1995). Placental transfer of sulindac and its active sulfide metabolite in humans. American Journal of Obstetrics and Gynecology. 172(3). 886–890. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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