Matthew S. O’Connor

2.2k total citations
22 papers, 1.6k citations indexed

About

Matthew S. O’Connor is a scholar working on Molecular Biology, Physiology and Surgery. According to data from OpenAlex, Matthew S. O’Connor has authored 22 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 6 papers in Physiology and 4 papers in Surgery. Recurrent topics in Matthew S. O’Connor's work include Telomeres, Telomerase, and Senescence (6 papers), Mitochondrial Function and Pathology (4 papers) and Metabolism and Genetic Disorders (3 papers). Matthew S. O’Connor is often cited by papers focused on Telomeres, Telomerase, and Senescence (6 papers), Mitochondrial Function and Pathology (4 papers) and Metabolism and Genetic Disorders (3 papers). Matthew S. O’Connor collaborates with scholars based in United States, Spain and Hungary. Matthew S. O’Connor's co-authors include Zhou Songyang, Jun Qin, Dan Liu, Huawei Xin, Dan Liu, Doug W. Chan, Hyeung Kim, Wen Sun, Ma Wan and Dan Liu and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Matthew S. O’Connor

22 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew S. O’Connor United States 11 1.2k 1.2k 211 160 113 22 1.6k
Hyeung Kim United States 15 1.1k 0.8× 691 0.6× 128 0.6× 131 0.8× 31 0.3× 19 1.3k
J M Rowson United Kingdom 17 859 0.7× 1.1k 0.9× 215 1.0× 270 1.7× 55 0.5× 47 1.7k
Laure Crabbé France 15 1.9k 1.6× 725 0.6× 118 0.6× 202 1.3× 23 0.2× 19 2.2k
Terence Davis United Kingdom 22 828 0.7× 386 0.3× 141 0.7× 56 0.3× 28 0.2× 58 1.4k
Erin J. Cram United States 20 790 0.6× 143 0.1× 367 1.7× 197 1.2× 34 0.3× 55 1.4k
Troy A. A. Harkness Canada 19 1.2k 0.9× 136 0.1× 263 1.2× 201 1.3× 32 0.3× 49 1.4k
Rika Kusumoto United States 14 2.3k 1.9× 424 0.3× 81 0.4× 231 1.4× 27 0.2× 17 3.0k
Corina Borghouts Germany 20 875 0.7× 91 0.1× 274 1.3× 83 0.5× 16 0.1× 29 1.2k
Douglas L. Pittman United States 15 1.6k 1.3× 152 0.1× 37 0.2× 237 1.5× 29 0.3× 27 1.9k
Ashley Craig United Kingdom 17 794 0.6× 131 0.1× 164 0.8× 33 0.2× 41 0.4× 23 1.2k

Countries citing papers authored by Matthew S. O’Connor

Since Specialization
Citations

This map shows the geographic impact of Matthew S. O’Connor'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 Matthew S. O’Connor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew S. O’Connor more than expected).

Fields of papers citing papers by Matthew S. O’Connor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew S. O’Connor. 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 Matthew S. O’Connor. The network helps show where Matthew S. O’Connor may publish in the future.

Co-authorship network of co-authors of Matthew S. O’Connor

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew S. O’Connor. A scholar is included among the top collaborators of Matthew S. O’Connor 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 Matthew S. O’Connor. Matthew S. O’Connor 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.
O’Connor, Matthew S., J.D. Pipkin, Milo Malanga, et al.. (2025). A comprehensive nomenclature system for cyclodextrins. Carbohydrate Polymers. 360. 123600–123600. 1 indexed citations
2.
Bhargava, Prerna, et al.. (2025). Selective removal of 7-ketocholesterol by a novel atherosclerosis therapeutic candidate reverts foam cells to a macrophage-like phenotype. Atherosclerosis. 409. 119217–119217. 1 indexed citations
3.
Piñeiro, Ángel, et al.. (2024). Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico. Computational and Structural Biotechnology Journal. 23. 1117–1128. 5 indexed citations
4.
García‐Fandiño, Rebeca, et al.. (2024). Unraveling the molecular dynamics of sugammadex-rocuronium complexation: A blueprint for cyclodextrin drug design. Carbohydrate Polymers. 334. 122018–122018. 5 indexed citations
5.
Manet, Ilse, Milo Malanga, Daniel M. Clemens, et al.. (2023). Addressing the complexities in measuring cyclodextrin-sterol binding constants: A multidimensional study. Carbohydrate Polymers. 323. 121360–121360. 7 indexed citations
6.
Malanga, Milo, et al.. (2021). Cyclodextrin dimers: A versatile approach to optimizing encapsulation and their application to therapeutic extraction of toxic oxysterols. International Journal of Pharmaceutics. 606. 120522–120522. 15 indexed citations
7.
O’Connor, Matthew S., et al.. (2020). Rapid enrichment of mitochondria from mammalian cell cultures using digitonin. MethodsX. 8. 101197–101197. 7 indexed citations
8.
O’Connor, Matthew S., et al.. (2020). Codon optimization is an essential parameter for the efficient allotopic expression of mtDNA genes. Redox Biology. 30. 101429–101429. 14 indexed citations
9.
Jerome, W. Gray, et al.. (2019). 7-Ketocholesterol in disease and aging. Redox Biology. 29. 101380–101380. 132 indexed citations
10.
Basisty, Nathan, et al.. (2016). Stable nuclear expression ofATP8andATP6genes rescues a mtDNA Complex Vnullmutant. Nucleic Acids Research. 44(19). gkw756–gkw756. 28 indexed citations
11.
O’Connor, Matthew S., Morgan E. Carlson, & Irina M. Conboy. (2009). Differentiation rather than aging of muscle stem cells abolishes their telomerase activity. Biotechnology Progress. 25(4). 1130–1137. 39 indexed citations
12.
Xin, Huawei, Dan Liu, Ma Wan, et al.. (2007). TPP1 is a homologue of ciliate TEBP-β and interacts with POT1 to recruit telomerase. Nature. 445(7127). 559–562. 367 indexed citations
13.
O’Connor, Matthew S., et al.. (2006). A critical role for TPP1 and TIN2 interaction in high-order telomeric complex assembly. Proceedings of the National Academy of Sciences. 103(32). 11874–11879. 195 indexed citations
14.
Liu, Dan, et al.. (2004). PTOP interacts with POT1 and regulates its localization to telomeres. Nature Cell Biology. 6(7). 673–680. 338 indexed citations
15.
O’Connor, Matthew S., et al.. (2004). The Human Rap1 Protein Complex and Modulation of Telomere Length. Journal of Biological Chemistry. 279(27). 28585–28591. 133 indexed citations
16.
Liu, Dan, Matthew S. O’Connor, Jun Qin, & Zhou Songyang. (2004). Telosome, a Mammalian Telomere-associated Complex Formed by Multiple Telomeric Proteins. Journal of Biological Chemistry. 279(49). 51338–51342. 307 indexed citations
17.
Rogelberg, Steven G., et al.. (2002). Using the stepladder technique to facilitate the performance of audioconferencing.. Journal of Applied Psychology. 87(5). 994–1000. 10 indexed citations
18.
Rogelberg, Steven G., et al.. (2002). Using the stepladder technique to facilitate the performance of audioconferencing.. Journal of Applied Psychology. 87(5). 994–1000. 1 indexed citations
19.
Bachiochi, Peter D., et al.. (2000). The Qualities of an Effective Team Leader. Organization development journal. 18(1). 11. 7 indexed citations
20.
Rogelberg, Steven G. & Matthew S. O’Connor. (1998). Extending the stepladder technique: An examination of self-paced stepladder groups.. Group Dynamics Theory Research and Practice. 2(2). 82–91. 10 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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