Thomas M. Stubbs

2.9k total citations · 1 hit paper
30 papers, 1.9k citations indexed

About

Thomas M. Stubbs is a scholar working on Molecular Biology, Obstetrics and Gynecology and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Thomas M. Stubbs has authored 30 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 12 papers in Obstetrics and Gynecology and 6 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Thomas M. Stubbs's work include Epigenetics and DNA Methylation (11 papers), Pregnancy and preeclampsia studies (7 papers) and Pluripotent Stem Cells Research (3 papers). Thomas M. Stubbs is often cited by papers focused on Epigenetics and DNA Methylation (11 papers), Pregnancy and preeclampsia studies (7 papers) and Pluripotent Stem Cells Research (3 papers). Thomas M. Stubbs collaborates with scholars based in United States, United Kingdom and Germany. Thomas M. Stubbs's co-authors include Wolf Reik, Felix Krueger, Oliver Stegle, John Lazarchick, Marc Jan Bonder, Stephen J. Clark, J. Peter Van Dorsten, Chantriolnt-Andreas Kapourani, Ricard Argelaguet and John C. Marioni and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and American Journal of Obstetrics and Gynecology.

In The Last Decade

Thomas M. Stubbs

30 papers receiving 1.8k citations

Hit Papers

scNMT-seq enables joint profiling of chromatin accessibil... 2018 2026 2020 2023 2018 100 200 300 400

Peers

Thomas M. Stubbs
Michael F. Wangler United States
Khursheed Iqbal United States
Jaya Nautiyal United Kingdom
Irene E. Zohn United States
Ryan W. Serra United States
Zoë Webster United Kingdom
Payel Sen United States
Michael F. Wangler United States
Thomas M. Stubbs
Citations per year, relative to Thomas M. Stubbs Thomas M. Stubbs (= 1×) peers Michael F. Wangler

Countries citing papers authored by Thomas M. Stubbs

Since Specialization
Citations

This map shows the geographic impact of Thomas M. Stubbs'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 Thomas M. Stubbs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas M. Stubbs more than expected).

Fields of papers citing papers by Thomas M. Stubbs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas M. Stubbs. 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 Thomas M. Stubbs. The network helps show where Thomas M. Stubbs may publish in the future.

Co-authorship network of co-authors of Thomas M. Stubbs

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas M. Stubbs. A scholar is included among the top collaborators of Thomas M. Stubbs 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 Thomas M. Stubbs. Thomas M. Stubbs 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.
Monahan, Jack, Paul G. O’Reilly, Manik Garg, et al.. (2025). The impact of artificial intelligence on biomarker discovery. Emerging Topics in Life Sciences. 8(2). 89–105. 1 indexed citations
2.
Bonder, Marc Jan, Stephen J. Clark, Felix Krueger, et al.. (2024). scEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood. Nature Communications. 15(1). 7567–7567. 4 indexed citations
3.
Gill, Diljeet, Aled Parry, Fátima Santos, et al.. (2022). Multi-omic rejuvenation of human cells by maturation phase transient reprogramming. eLife. 11. 90 indexed citations
4.
Evano, Brendan, Diljeet Gill, Irene Hernando-Herraez, et al.. (2020). Transcriptome and epigenome diversity and plasticity of muscle stem cells following transplantation. PLoS Genetics. 16(10). e1009022–e1009022. 25 indexed citations
5.
Hernando-Herraez, Irene, Brendan Evano, Thomas M. Stubbs, et al.. (2019). Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells. Nature Communications. 10(1). 4361–4361. 155 indexed citations
6.
Martin‐Herranz, Daniel E., Erfan Aref‐Eshghi, Marc Jan Bonder, et al.. (2019). Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1. Genome biology. 20(1). 146–146. 54 indexed citations
7.
Clark, Stephen J., Ricard Argelaguet, Chantriolnt-Andreas Kapourani, et al.. (2018). scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nature Communications. 9(1). 781–781. 437 indexed citations breakdown →
8.
Hãhn, Oliver, Thomas M. Stubbs, Wolf Reik, et al.. (2018). Hepatic gene body hypermethylation is a shared epigenetic signature of murine longevity. PLoS Genetics. 14(11). e1007766–e1007766. 8 indexed citations
9.
Stubbs, Thomas M., Marc Jan Bonder, Anne‐Katrien Stark, et al.. (2017). Multi-tissue DNA methylation age predictor in mouse. Genome biology. 18(1). 68–68. 255 indexed citations
10.
Milagre, Inês, Thomas M. Stubbs, Michelle King, et al.. (2017). Gender Differences in Global but Not Targeted Demethylation in iPSC Reprogramming. Cell Reports. 18(5). 1079–1089. 38 indexed citations
11.
Martin‐Herranz, Daniel E., António J. M. Ribeiro, Felix Krueger, et al.. (2017). cuRRBS: simple and robust evaluation of enzyme combinations for reduced representation approaches. Nucleic Acids Research. 45(20). 11559–11569. 10 indexed citations
12.
Hãhn, Oliver, Sebastian Grönke, Thomas M. Stubbs, et al.. (2017). Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism. Genome biology. 18(1). 56–56. 172 indexed citations
13.
Eckersley-Maslin, Mélanie, Valentine Svensson, Christel Krueger, et al.. (2016). MERVL/Zscan4 Network Activation Results in Transient Genome-wide DNA Demethylation of mESCs. Cell Reports. 17(1). 179–192. 150 indexed citations
14.
Stubbs, Thomas M.. (2000). Oxytocin for Labor Induction. Clinical Obstetrics & Gynecology. 43(3). 489–494. 22 indexed citations
15.
Rust, Orion A., James A. Bofill, Michael E. Andrew, et al.. (1996). Lowering the threshold for the diagnosis of gestational diabetes. American Journal of Obstetrics and Gynecology. 175(4). 961–965. 19 indexed citations
16.
Stubbs, Thomas M., et al.. (1994). Antenatal vitamin K therapy of the low-birth-weight infant. American Journal of Obstetrics and Gynecology. 170(1). 85–89. 10 indexed citations
17.
Stubbs, Thomas M., et al.. (1992). Fetal maternal laryngeal papillomatosis in pregnancy:A case report. American Journal of Obstetrics and Gynecology. 166(2). 524–525. 5 indexed citations
18.
Müller, P, Thomas M. Stubbs, & Sherry Laurent. (1992). A prospective randomized clinical trial comparing two oxytocin induction protocols. American Journal of Obstetrics and Gynecology. 167(2). 373–381. 34 indexed citations
19.
Lazarchick, John, et al.. (1986). Predictive value of fibronectin levels in normotensive gravid women destined to become preeclamptic. American Journal of Obstetrics and Gynecology. 154(5). 1050–1052. 62 indexed citations
20.
Stubbs, Thomas M., John Lazarchick, & Edgar O. Horger. (1984). Plasma fibronectin levels in preeclampsia: A possible biochemical marker for vascular endothelial damage. American Journal of Obstetrics and Gynecology. 150(7). 885–887. 97 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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