Ryan M. Haskins

2.5k total citations · 1 hit paper
8 papers, 1.2k citations indexed

About

Ryan M. Haskins is a scholar working on Molecular Biology, Immunology and Rheumatology. According to data from OpenAlex, Ryan M. Haskins has authored 8 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Immunology and 2 papers in Rheumatology. Recurrent topics in Ryan M. Haskins's work include Kruppel-like factors research (3 papers), Heterotopic Ossification and Related Conditions (2 papers) and Cancer-related molecular mechanisms research (2 papers). Ryan M. Haskins is often cited by papers focused on Kruppel-like factors research (3 papers), Heterotopic Ossification and Related Conditions (2 papers) and Cancer-related molecular mechanisms research (2 papers). Ryan M. Haskins collaborates with scholars based in United States, India and Estonia. Ryan M. Haskins's co-authors include Gary K. Owens, Gabriel F. Alencar, Olga A. Cherepanova, Laura S. Shankman, Brant E. Isakson, Elizabeth S. Greene, Delphine Gomez, Pamela Swiatlowska, Morgan Salmon and Alexandra Newman and has published in prestigious journals such as Circulation, Nature Medicine and Arteriosclerosis Thrombosis and Vascular Biology.

In The Last Decade

Ryan M. Haskins

7 papers receiving 1.2k citations

Hit Papers

KLF4-dependent phenotypic modulation of smooth muscle cel... 2015 2026 2018 2022 2015 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ryan M. Haskins United States 7 673 530 231 167 132 8 1.2k
Alexandra Newman United States 6 660 1.0× 588 1.1× 214 0.9× 185 1.1× 179 1.4× 11 1.2k
Laura S. Shankman United States 14 943 1.4× 702 1.3× 305 1.3× 208 1.2× 162 1.2× 17 1.6k
Joel Chappell United Kingdom 12 580 0.9× 331 0.6× 163 0.7× 116 0.7× 89 0.7× 18 976
Yevgenia Tesmenitsky United States 16 691 1.0× 619 1.2× 480 2.1× 212 1.3× 167 1.3× 22 1.4k
Richard A. Baylis United States 13 464 0.7× 562 1.1× 118 0.5× 165 1.0× 136 1.0× 22 1.0k
Curran Murphy United States 8 594 0.9× 344 0.6× 241 1.0× 171 1.0× 154 1.2× 12 1.2k
Daniel DiRenzo United States 15 478 0.7× 474 0.9× 121 0.5× 157 0.9× 102 0.8× 38 1.0k
Ela Karshovska Germany 13 478 0.7× 298 0.6× 252 1.1× 114 0.7× 76 0.6× 17 871
Laura A. Maile United States 28 874 1.3× 317 0.6× 307 1.3× 132 0.8× 65 0.5× 48 1.5k
Jan‐Marcus Daniel Germany 15 609 0.9× 172 0.3× 191 0.8× 106 0.6× 50 0.4× 31 992

Countries citing papers authored by Ryan M. Haskins

Since Specialization
Citations

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

Fields of papers citing papers by Ryan M. Haskins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan M. Haskins

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

All Works

8 of 8 papers shown
1.
Asgharian, Bahman, et al.. (2024). The fate of an inhaled cigarette puff in the human respiratory tract. Inhalation Toxicology. 36(6). 378–390.
2.
Strong, Amy L., Philip Spreadborough, Devaveena Dey, et al.. (2020). BMP Ligand Trap ALK3-Fc Attenuates Osteogenesis and Heterotopic Ossification in Blast-Related Lower Extremity Trauma. Stem Cells and Development. 30(2). 91–105. 24 indexed citations
4.
Alencar, Gabriel F., Katherine Owsiany, Santosh Karnewar, et al.. (2020). Stem Cell Pluripotency Genes Klf4 and Oct4 Regulate Complex SMC Phenotypic Changes Critical in Late-Stage Atherosclerotic Lesion Pathogenesis. Circulation. 142(21). 2045–2059. 234 indexed citations
5.
Bulut, Gamze B., Gabriel F. Alencar, Katherine Owsiany, et al.. (2020). KLF4 (Kruppel-Like Factor 4)-Dependent Perivascular Plasticity Contributes to Adipose Tissue inflammation. Arteriosclerosis Thrombosis and Vascular Biology. 41(1). 284–301. 23 indexed citations
6.
Cherepanova, Olga A., Prasad Srikakulapu, Elizabeth S. Greene, et al.. (2019). Novel Autoimmune IgM Antibody Attenuates Atherosclerosis in IgM Deficient Low-Fat Diet–Fed, but Not Western Diet–Fed Apoe –/– Mice. Arteriosclerosis Thrombosis and Vascular Biology. 40(1). 206–219. 18 indexed citations
7.
Haskins, Ryan M., Anh T. Nguyen, Gabriel F. Alencar, et al.. (2018). Klf4 has an unexpected protective role in perivascular cells within the microvasculature. American Journal of Physiology-Heart and Circulatory Physiology. 315(2). H402–H414. 15 indexed citations
8.
Shankman, Laura S., Delphine Gomez, Olga A. Cherepanova, et al.. (2015). KLF4-dependent phenotypic modulation of smooth muscle cells has a key role in atherosclerotic plaque pathogenesis. Nature Medicine. 21(6). 628–637. 843 indexed citations breakdown →

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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