Kyle M. Kovary

1.1k total citations · 1 hit paper
9 papers, 608 citations indexed

About

Kyle M. Kovary is a scholar working on Molecular Biology, Physiology and Epidemiology. According to data from OpenAlex, Kyle M. Kovary has authored 9 papers receiving a total of 608 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Physiology and 2 papers in Epidemiology. Recurrent topics in Kyle M. Kovary's work include Gene Regulatory Network Analysis (3 papers), Adipose Tissue and Metabolism (3 papers) and Adipokines, Inflammation, and Metabolic Diseases (2 papers). Kyle M. Kovary is often cited by papers focused on Gene Regulatory Network Analysis (3 papers), Adipose Tissue and Metabolism (3 papers) and Adipokines, Inflammation, and Metabolic Diseases (2 papers). Kyle M. Kovary collaborates with scholars based in United States. Kyle M. Kovary's co-authors include Mary N. Teruel, Zhen Shi, Kotaro Fujii, Naomi R. Genuth, Hannes Röst, Maria Barna, Robert Ahrends, Asuka Ota, Byung Ouk Park and Takamasa Kudo and has published in prestigious journals such as Science, Journal of Biological Chemistry and Molecular Cell.

In The Last Decade

Kyle M. Kovary

9 papers receiving 608 citations

Hit Papers

Heterogeneous Ribosomes Preferentially Translate Distinct... 2017 2026 2020 2023 2017 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kyle M. Kovary United States 7 514 61 56 53 50 9 608
Nickolaos Nikiforos Giakoumakis Greece 10 367 0.7× 94 1.5× 52 0.9× 57 1.1× 37 0.7× 13 482
Xizi Chen China 12 622 1.2× 48 0.8× 38 0.7× 36 0.7× 35 0.7× 18 719
Joanna Boros United Kingdom 11 498 1.0× 60 1.0× 102 1.8× 85 1.6× 39 0.8× 13 622
Armando Aranda‐Anzaldo Mexico 16 368 0.7× 83 1.4× 79 1.4× 40 0.8× 50 1.0× 48 523
Christopher M. Gallo United States 7 490 1.0× 36 0.6× 68 1.2× 74 1.4× 54 1.1× 9 696
Miki Ii United States 13 639 1.2× 123 2.0× 37 0.7× 38 0.7× 52 1.0× 14 706
Gabriela Imreh Sweden 12 666 1.3× 133 2.2× 75 1.3× 26 0.5× 74 1.5× 19 845
Xiaoduo Xie China 10 490 1.0× 86 1.4× 81 1.4× 25 0.5× 87 1.7× 18 662
Yaxue Zeng United States 7 780 1.5× 44 0.7× 96 1.7× 32 0.6× 95 1.9× 10 858
Zhanyu Ding China 11 558 1.1× 81 1.3× 70 1.3× 16 0.3× 92 1.8× 13 617

Countries citing papers authored by Kyle M. Kovary

Since Specialization
Citations

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

Fields of papers citing papers by Kyle M. Kovary

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kyle M. Kovary

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

All Works

9 of 9 papers shown
1.
Kudo, Takamasa, et al.. (2023). Context-dependent regulation of lipid accumulation in adipocytes by a HIF1α-PPARγ feedback network. Cell Systems. 14(12). 1074–1086.e7. 6 indexed citations
2.
Tholen, Stefan, Roma Patel, Kyle M. Kovary, et al.. (2022). Flattening of circadian glucocorticoid oscillations drives acute hyperinsulinemia and adipocyte hypertrophy. Cell Reports. 39(13). 111018–111018. 12 indexed citations
3.
Kovary, Kyle M., et al.. (2020). Molecular Competition in G1 Controls When Cells Simultaneously Commit to Terminally Differentiate and Exit the Cell Cycle. Cell Reports. 31(11). 107769–107769. 26 indexed citations
4.
Kovary, Kyle M., et al.. (2018). Expression variation and covariation impair analog and enable binary signaling control. Molecular Systems Biology. 14(5). e7997–e7997. 9 indexed citations
5.
Shi, Zhen, Kotaro Fujii, Kyle M. Kovary, et al.. (2017). Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide. Molecular Cell. 67(1). 71–83.e7. 413 indexed citations breakdown →
6.
Ota, Asuka, Kyle M. Kovary, Robert Ahrends, et al.. (2015). Using SRM-MS to quantify nuclear protein abundance differences between adipose tissue depots of insulin-resistant mice. Journal of Lipid Research. 56(5). 1068–1078. 11 indexed citations
7.
Teruel, Mary N., Robert Ahrends, Asuka Ota, Kyle M. Kovary, & Byung Ouk Park. (2014). Controlling low rates of terminal cell differentiation through noise and ultra‐high feedback (981.1). The FASEB Journal. 28(S1). 1 indexed citations
8.
Ahrends, Robert, Asuka Ota, Kyle M. Kovary, et al.. (2014). Controlling low rates of cell differentiation through noise and ultrahigh feedback. Science. 344(6190). 1384–1389. 68 indexed citations
9.
Kovary, Kyle M., et al.. (2013). The E3 Ubiquitin Ligase UBE3C Enhances Proteasome Processivity by Ubiquitinating Partially Proteolyzed Substrates. Journal of Biological Chemistry. 288(48). 34575–34587. 62 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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