Steve Perrin

3.6k total citations · 2 hit papers
9 papers, 1.5k citations indexed

About

Steve Perrin is a scholar working on Genetics, Neurology and Molecular Biology. According to data from OpenAlex, Steve Perrin has authored 9 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Genetics, 3 papers in Neurology and 2 papers in Molecular Biology. Recurrent topics in Steve Perrin's work include Neurogenetic and Muscular Disorders Research (3 papers), Amyotrophic Lateral Sclerosis Research (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Steve Perrin is often cited by papers focused on Neurogenetic and Muscular Disorders Research (3 papers), Amyotrophic Lateral Sclerosis Research (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Steve Perrin collaborates with scholars based in United States, Switzerland and Germany. Steve Perrin's co-authors include R. Blake Pepinsky, Melissa Levesque, John McCoy, Sha Mi, Richard L. Cate, Xinhua Lee, Jane K. Relton, Bryan Sands, Norm Allaire and Greg Thill and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Neuroscience.

In The Last Decade

Steve Perrin

8 papers receiving 1.5k citations

Hit Papers

LINGO-1 is a component of the Nogo-66 receptor/p75 signal... 2004 2026 2011 2018 2004 2014 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Steve Perrin United States 7 632 549 361 288 158 9 1.5k
Peter H. Larsen Canada 19 762 1.2× 433 0.8× 244 0.7× 320 1.1× 100 0.6× 28 2.1k
Carola Krüger Germany 16 518 0.8× 324 0.6× 331 0.9× 270 0.9× 233 1.5× 21 1.5k
Jacqueline L. Vanderluit Canada 25 1.2k 1.9× 366 0.7× 305 0.8× 126 0.4× 100 0.6× 33 1.8k
Kazuhiko Namikawa Japan 17 736 1.2× 534 1.0× 214 0.6× 135 0.5× 92 0.6× 20 1.4k
Anna Robeva United States 18 870 1.4× 659 1.2× 180 0.5× 99 0.3× 130 0.8× 40 1.9k
Fatima Banine United States 15 914 1.4× 254 0.5× 350 1.0× 243 0.8× 52 0.3× 23 1.8k
R. Kolbeck United States 15 522 0.8× 745 1.4× 428 1.2× 80 0.3× 80 0.5× 23 1.6k
Tzong‐Shiue Yu United States 16 1.2k 1.9× 332 0.6× 584 1.6× 209 0.7× 612 3.9× 18 2.6k
James P. Fandl United States 7 1.5k 2.3× 874 1.6× 420 1.2× 119 0.4× 113 0.7× 8 2.9k

Countries citing papers authored by Steve Perrin

Since Specialization
Citations

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

Fields of papers citing papers by Steve Perrin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Steve Perrin. 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 Steve Perrin. The network helps show where Steve Perrin may publish in the future.

Co-authorship network of co-authors of Steve Perrin

This figure shows the co-authorship network connecting the top 25 collaborators of Steve Perrin. A scholar is included among the top collaborators of Steve Perrin 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 Steve Perrin. Steve Perrin 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.
Anwar, Imran J., Dora M. Berman, Isabel DeLaura, et al.. (2023). The anti-CD40L monoclonal antibody AT-1501 promotes islet and kidney allograft survival and function in nonhuman primates. Science Translational Medicine. 15(711). eadf6376–eadf6376. 26 indexed citations
3.
Maier, Marcel, Tobias Welt, Fabio Montrasio, et al.. (2018). A human-derived antibody targets misfolded SOD1 and ameliorates motor symptoms in mouse models of amyotrophic lateral sclerosis. Science Translational Medicine. 10(470). 58 indexed citations
4.
Perrin, Steve. (2014). Preclinical research: Make mouse studies work. Nature. 507(7493). 423–425. 459 indexed citations breakdown →
5.
Hatzipetros, Theo, Laurent Bogdanik, Joshua D. Kidd, et al.. (2013). C57BL/6J congenic Prp-TDP43A315T mice develop progressive neurodegeneration in the myenteric plexus of the colon without exhibiting key features of ALS. Brain Research. 1584. 59–72. 76 indexed citations
6.
Appel, Stanley H., Anatoly Chernyshev, Patrizia Fanara, et al.. (2009). Alzheimer Research Forum Live Discussion: Mice on Trial? Issues in the Design of Drug Studies. Journal of Alzheimer s Disease. 16(1). 197–205. 1 indexed citations
7.
Wang, Jiou, George W. Farr, Caroline J. Zeiss, et al.. (2009). Progressive aggregation despite chaperone associations of a mutant SOD1-YFP in transgenic mice that develop ALS. Proceedings of the National Academy of Sciences. 106(5). 1392–1397. 118 indexed citations
8.
Perrin, Steve, Brian Elenbaas, Stephen E. Fawell, et al.. (2005). Identification of SFRP1 as a Candidate Mediator of Stromal-to-Epithelial Signaling in Prostate Cancer. Cancer Research. 65(22). 10423–10430. 129 indexed citations
9.
Mi, Sha, Xinhua Lee, Zhaohui Shao, et al.. (2004). LINGO-1 is a component of the Nogo-66 receptor/p75 signaling complex. Nature Neuroscience. 7(3). 221–228. 660 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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