Samuel M. Peterson

1.1k total citations
26 papers, 840 citations indexed

About

Samuel M. Peterson is a scholar working on Molecular Biology, Genetics and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Samuel M. Peterson has authored 26 papers receiving a total of 840 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 7 papers in Genetics and 6 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Samuel M. Peterson's work include Environmental Toxicology and Ecotoxicology (4 papers), Congenital heart defects research (4 papers) and MicroRNA in disease regulation (4 papers). Samuel M. Peterson is often cited by papers focused on Environmental Toxicology and Ecotoxicology (4 papers), Congenital heart defects research (4 papers) and MicroRNA in disease regulation (4 papers). Samuel M. Peterson collaborates with scholars based in United States, United Kingdom and Malawi. Samuel M. Peterson's co-authors include Jennifer L. Freeman, Gregory J. Weber, Jun Zhang, Marı́a S. Sepúlveda, Betsy Ferguson, John H. Postlethwait, Ingo Braasch, Peter Batzel, Braedan M. McCluskey and Thomas Desvignes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Development and Environmental Health Perspectives.

In The Last Decade

Samuel M. Peterson

26 papers receiving 836 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samuel M. Peterson United States 15 317 266 219 119 106 26 840
Rasoul Nourizadeh-Lillabadi Norway 20 188 0.6× 338 1.3× 107 0.5× 229 1.9× 114 1.1× 41 1.0k
Angèle Tingaud‐Sequeira France 20 418 1.3× 138 0.5× 185 0.8× 324 2.7× 91 0.9× 32 1.2k
Ståle Ellingsen Norway 14 565 1.8× 195 0.7× 133 0.6× 125 1.1× 27 0.3× 26 882
Christopher Ton United States 13 502 1.6× 162 0.6× 534 2.4× 114 1.0× 61 0.6× 17 1.3k
Francesca Benato Italy 15 262 0.8× 135 0.5× 178 0.8× 124 1.0× 41 0.4× 19 775
Andrew Dodd New Zealand 15 730 2.3× 111 0.4× 377 1.7× 249 2.1× 60 0.6× 29 1.4k
Igor Kondrychyn Singapore 15 483 1.5× 71 0.3× 258 1.2× 124 1.0× 33 0.3× 23 779
Allison B. Coffin United States 21 360 1.1× 81 0.3× 240 1.1× 33 0.3× 190 1.8× 57 1.5k
Shelby L. Steele Canada 19 330 1.0× 57 0.2× 187 0.9× 58 0.5× 202 1.9× 27 1.0k
Jenny R. Lenkowski United States 6 325 1.0× 294 1.1× 111 0.5× 56 0.5× 18 0.2× 7 686

Countries citing papers authored by Samuel M. Peterson

Since Specialization
Citations

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

Fields of papers citing papers by Samuel M. Peterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samuel M. Peterson

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel M. Peterson. A scholar is included among the top collaborators of Samuel M. Peterson 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 Samuel M. Peterson. Samuel M. Peterson 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.
Vallender, Eric J., Charlotte E. Hotchkiss, Anne D. Lewis, et al.. (2023). Nonhuman primate genetic models for the study of rare diseases. Orphanet Journal of Rare Diseases. 18(1). 20–20. 12 indexed citations
2.
Sanghvi, Rashesh, et al.. (2023). A naturally occurring variant of MBD4 causes maternal germline hypermutation in primates. Genome Research. 33(12). 2053–2059. 7 indexed citations
3.
Peterson, Samuel M., Marina M. Watowich, Lauren Renner, et al.. (2023). Genetic variants in melanogenesis proteins TYRP1 and TYR are associated with the golden rhesus macaque phenotype. G3 Genes Genomes Genetics. 13(10). 2 indexed citations
4.
Brombin, Alessandro, et al.. (2022). Aldh2 is a lineage-specific metabolic gatekeeper in melanocyte stem cells. Development. 149(10). 8 indexed citations
5.
Sherman, Larry S., et al.. (2021). A novel non-human primate model of Pelizaeus-Merzbacher disease. Neurobiology of Disease. 158. 105465–105465. 7 indexed citations
6.
7.
Peterson, Samuel M., Trevor J. McGill, Teresa Puthussery, et al.. (2019). Bardet-Biedl Syndrome in rhesus macaques: A nonhuman primate model of retinitis pigmentosa. Experimental Eye Research. 189. 107825–107825. 34 indexed citations
8.
Horzmann, Katharine A., Victoria Hedrick, Tiago J. P. Sobreira, et al.. (2018). Embryonic atrazine exposure elicits proteomic, behavioral, and brain abnormalities with developmental time specific gene expression signatures. Journal of Proteomics. 186. 71–82. 35 indexed citations
9.
McBride, Jodi L., Martha Neuringer, Betsy Ferguson, et al.. (2018). Discovery of a CLN7 model of Batten disease in non-human primates. Neurobiology of Disease. 119. 65–78. 27 indexed citations
10.
Bimber, Benjamin N., et al.. (2017). Whole genome sequencing predicts novel human disease models in rhesus macaques. Genomics. 109(3-4). 214–220. 21 indexed citations
11.
12.
Freeman, Jennifer L., Gregory J. Weber, Samuel M. Peterson, & Linda H. Nie. (2014). Embryonic ionizing radiation exposure results in expression alterations of genes associated with cardiovascular and neurological development, function, and disease and modified cardiovascular function in zebrafish. Frontiers in Genetics. 5. 268–268. 32 indexed citations
13.
Braasch, Ingo, Samuel M. Peterson, Thomas Desvignes, et al.. (2014). A new model army: Emerging fish models to study the genomics of vertebrate Evo‐Devo. Journal of Experimental Zoology Part B Molecular and Developmental Evolution. 324(4). 316–341. 74 indexed citations
14.
Peterson, Samuel M., Jun Zhang, & Jennifer L. Freeman. (2013). Developmental reelin expression and time point-specific alterations from lead exposure in zebrafish. Neurotoxicology and Teratology. 38. 53–60. 33 indexed citations
15.
16.
Zhang, Jun, Samuel M. Peterson, Gregory J. Weber, et al.. (2011). Decreased axonal density and altered expression profiles of axonal guidance genes underlying lead (Pb) neurodevelopmental toxicity at early embryonic stages in the zebrafish. Neurotoxicology and Teratology. 33(6). 715–720. 54 indexed citations
17.
Peterson, Samuel M., Jun Zhang, Gregory J. Weber, & Jennifer L. Freeman. (2010). Global Gene Expression Analysis Reveals Dynamic and Developmental Stage–Dependent Enrichment of Lead-Induced Neurological Gene Alterations. Environmental Health Perspectives. 119(5). 615–621. 63 indexed citations
18.
Peterson, Samuel M. & Jennifer L. Freeman. (2009). Cancer Cytogenetics in the Zebrafish. Zebrafish. 6(4). 355–360. 10 indexed citations
19.
Peterson, Samuel M. & Jennifer L. Freeman. (2009). Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform. Journal of Visualized Experiments. 2 indexed citations
20.
Peterson, Samuel M. & Jennifer L. Freeman. (2009). RNA Isolation from Embryonic Zebrafish and cDNA Synthesis for Gene Expression Analysis. Journal of Visualized Experiments. 88 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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