Mark T. Mc Auley

1.5k total citations
38 papers, 980 citations indexed

About

Mark T. Mc Auley is a scholar working on Molecular Biology, Surgery and Aging. According to data from OpenAlex, Mark T. Mc Auley has authored 38 papers receiving a total of 980 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 10 papers in Surgery and 8 papers in Aging. Recurrent topics in Mark T. Mc Auley's work include Epigenetics and DNA Methylation (8 papers), Genetics, Aging, and Longevity in Model Organisms (8 papers) and Cholesterol and Lipid Metabolism (8 papers). Mark T. Mc Auley is often cited by papers focused on Epigenetics and DNA Methylation (8 papers), Genetics, Aging, and Longevity in Model Organisms (8 papers) and Cholesterol and Lipid Metabolism (8 papers). Mark T. Mc Auley collaborates with scholars based in United Kingdom, United States and Australia. Mark T. Mc Auley's co-authors include A. Morgan, Kathleen M. Mooney, Trevor J. Davies, Stuart Wilkinson, Darren J. Wilkinson, Carole J. Proctor, Neil Q. McDonald, Thomas B. L. Kirkwood, J. Enrique Salcedo-Sora and Loukas Zagkos and has published in prestigious journals such as BMC Bioinformatics, Journal of Theoretical Biology and Microbiology.

In The Last Decade

Mark T. Mc Auley

37 papers receiving 966 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark T. Mc Auley United Kingdom	18	465	219	177	133	105	38	980
Lin Dou China	13	540 1.2×	110 0.5×	293 1.7×	150 1.1×	105 1.0×	25	1.3k
Karthikeyani Chellappa United States	17	645 1.4×	148 0.7×	328 1.9×	95 0.7×	43 0.4×	20	1.5k
Jinling Hou United States	23	994 2.1×	148 0.7×	277 1.6×	110 0.8×	140 1.3×	28	1.7k
Jinhua Shen China	17	577 1.2×	233 1.1×	222 1.3×	138 1.0×	22 0.2×	69	1.4k
Mingjing Yan China	10	467 1.0×	63 0.3×	266 1.5×	97 0.7×	105 1.0×	18	1.1k
Benoît Pourcet France	15	503 1.1×	195 0.9×	281 1.6×	73 0.5×	40 0.4×	23	1.1k
Alberto Díaz‐Ruiz Spain	23	601 1.3×	149 0.7×	558 3.2×	175 1.3×	75 0.7×	42	1.5k
Jianbo Ni China	21	372 0.8×	391 1.8×	111 0.6×	96 0.7×	31 0.3×	50	1.1k
Nicola D. Kerrison United Kingdom	16	670 1.4×	92 0.4×	206 1.2×	62 0.5×	38 0.4×	23	1.2k
Stefano Di Biase Italy	11	385 0.8×	68 0.3×	701 4.0×	137 1.0×	115 1.1×	13	1.3k

Countries citing papers authored by Mark T. Mc Auley

Since Specialization

Citations

This map shows the geographic impact of Mark T. Mc Auley'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 Mark T. Mc Auley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark T. Mc Auley more than expected).

Fields of papers citing papers by Mark T. Mc Auley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark T. Mc Auley. 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 Mark T. Mc Auley. The network helps show where Mark T. Mc Auley may publish in the future.

Co-authorship network of co-authors of Mark T. Mc Auley

This figure shows the co-authorship network connecting the top 25 collaborators of Mark T. Mc Auley. A scholar is included among the top collaborators of Mark T. Mc Auley 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 Mark T. Mc Auley. Mark T. Mc Auley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Auley, Mark T. Mc & A. Morgan. (2025). Investigating Aging and DNA Methylation: A Path to Improving HealthSpan?. The Yale Journal of Biology and Medicine. 98(2). 237–244.

Auley, Mark T. Mc. (2024). The evolution of ageing: classic theories and emerging ideas. Biogerontology. 26(1). 6–6. 2 indexed citations

Morgan, A., et al.. (2023). Computationally Modelling Cholesterol Metabolism and Atherosclerosis. Biology. 12(8). 1133–1133. 1 indexed citations

Morgan, A., J. Enrique Salcedo-Sora, & Mark T. Mc Auley. (2023). A new mathematical model of folate homeostasis in E. coli highlights the potential importance of the folinic acid futile cycle in cell growth. Biosystems. 235. 105088–105088. 1 indexed citations

Auley, Mark T. Mc. (2022). Dietary restriction and ageing: Recent evolutionary perspectives. Mechanisms of Ageing and Development. 208. 111741–111741. 10 indexed citations

Morgan, A., Taranjit Singh, Victoria McGilligan, et al.. (2021). The Interdependency and Co-Regulation of the Vitamin D and Cholesterol Metabolism. Cells. 10(8). 2007–2007. 33 indexed citations

Auley, Mark T. Mc. (2021). DNA methylation in genes associated with the evolution of ageing and disease: A critical review. Ageing Research Reviews. 72. 101488–101488. 18 indexed citations

Zagkos, Loukas, et al.. (2018). Mathematical models of DNA methylation dynamics: Implications for health and ageing. Journal of Theoretical Biology. 462. 184–193. 18 indexed citations

Morgan, A., Trevor J. Davies, & Mark T. Mc Auley. (2018). The role of DNA methylation in ageing and cancer. Proceedings of The Nutrition Society. 77(4). 412–422. 159 indexed citations

10.

Auley, Mark T. Mc & Kathleen M. Mooney. (2017). LDL-C levels in older people: Cholesterol homeostasis and the free radical theory of ageing converge. Medical Hypotheses. 104. 15–19. 19 indexed citations

11.

Salcedo-Sora, J. Enrique & Mark T. Mc Auley. (2016). A mathematical model of microbial folate biosynthesis and utilisation: implications for antifolate development. Molecular BioSystems. 12(3). 923–933. 11 indexed citations

12.

Auley, Mark T. Mc, Kathleen M. Mooney, & J. Enrique Salcedo-Sora. (2016). Computational modelling folate metabolism and DNA methylation: implications for understanding health and ageing. Briefings in Bioinformatics. 19(2). bbw116–bbw116. 26 indexed citations

13.

Morgan, A., et al.. (2016). Investigating cholesterol metabolism and ageing using a systems biology approach. Proceedings of The Nutrition Society. 76(3). 378–391. 17 indexed citations

14.

Morgan, A., et al.. (2016). Mathematically modelling the dynamics of cholesterol metabolism and ageing. Biosystems. 145. 19–32. 20 indexed citations

15.

Auley, Mark T. Mc, Kathleen M. Mooney, Peter Angell, & Stephen J. Wilkinson. (2015). Mathematical Modelling of Metabolic Regulation in Aging. Metabolites. 5(2). 232–251. 19 indexed citations

16.

Auley, Mark T. Mc & Kathleen M. Mooney. (2014). Computational Systems Biology for Aging Research. PubMed. 40. 35–48. 16 indexed citations

17.

Auley, Mark T. Mc & Kathleen M. Mooney. (2014). Computationally Modeling Lipid Metabolism and Aging: A Mini-review. Computational and Structural Biotechnology Journal. 13. 38–46. 42 indexed citations

18.

Auley, Mark T. Mc. (2013). Nutrition Research and the Impact of Computational Systems Biology. Journal of Computer Science & Systems Biology. 6(5). 27 indexed citations

19.

Auley, Mark T. Mc, et al.. (2012). A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation. BMC Systems Biology. 6(1). 130–130. 71 indexed citations

20.

Auley, Mark T. Mc, et al.. (2005). Modelling Lipid Metabolism to Improve Healthy Ageing. BMC Bioinformatics. 6(S3). P21–29, S1. 9 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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