Sam Zaremba

2.5k total citations · 1 hit paper
19 papers, 1.6k citations indexed

About

Sam Zaremba is a scholar working on Molecular Biology, Cell Biology and Immunology. According to data from OpenAlex, Sam Zaremba has authored 19 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Cell Biology and 5 papers in Immunology. Recurrent topics in Sam Zaremba's work include vaccines and immunoinformatics approaches (5 papers), Immunotherapy and Immune Responses (5 papers) and T-cell and B-cell Immunology (5 papers). Sam Zaremba is often cited by papers focused on vaccines and immunoinformatics approaches (5 papers), Immunotherapy and Immune Responses (5 papers) and T-cell and B-cell Immunology (5 papers). Sam Zaremba collaborates with scholars based in United States. Sam Zaremba's co-authors include Susan Hockfield, Richard H. Scheuermann, Edward B. Klem, Sanjeev Kumar, Brett E Pickett, Yun Zhang, Li‐Wei Zhou, Victoria Hunt, R. Burke Squires and Robert G. Kalb and has published in prestigious journals such as Nucleic Acids Research, Neuron and Bioinformatics.

In The Last Decade

Sam Zaremba

19 papers receiving 1.5k citations

Hit Papers

ViPR: an open bioinformat... 2011 2026 2016 2021 2011 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
Sam Zaremba United States 13 728 412 327 321 171 19 1.6k
Guy Lemay Canada 25 662 0.9× 740 1.8× 261 0.8× 271 0.8× 97 0.6× 80 1.7k
Richard Peluso United States 27 1.2k 1.6× 478 1.2× 192 0.6× 936 2.9× 91 0.5× 38 2.7k
Shunji Sugii Japan 23 797 1.1× 429 1.0× 409 1.3× 127 0.4× 74 0.4× 101 1.9k
Rodney Colina Uruguay 23 525 0.7× 883 2.1× 233 0.7× 497 1.5× 181 1.1× 99 2.2k
Maik J. Lehmann Germany 17 582 0.8× 229 0.6× 372 1.1× 381 1.2× 234 1.4× 28 1.6k
Penny P. Powell United Kingdom 24 670 0.9× 244 0.6× 395 1.2× 313 1.0× 219 1.3× 42 1.8k
Hussein Y. Naim Switzerland 23 609 0.8× 573 1.4× 294 0.9× 972 3.0× 201 1.2× 32 1.9k
Viviana Falcón Cuba 21 563 0.8× 309 0.8× 186 0.6× 319 1.0× 59 0.3× 72 1.5k
Yasuhiko Horiguchi Japan 30 1.7k 2.4× 746 1.8× 473 1.4× 316 1.0× 317 1.9× 98 3.4k
Nakaba Sugimoto Japan 24 907 1.2× 569 1.4× 362 1.1× 273 0.9× 171 1.0× 50 2.1k

Countries citing papers authored by Sam Zaremba

Since Specialization
Citations

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

Fields of papers citing papers by Sam Zaremba

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sam Zaremba

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

All Works

19 of 19 papers shown
1.
Larsen, Christopher N., Guangyu Sun, Xiaomei Li, et al.. (2019). Mat_peptide: comprehensive annotation of mature peptides from polyproteins in five virus families. Bioinformatics. 36(5). 1627–1628. 2 indexed citations
2.
Squires, R. Burke, Victoria Hunt, Adolfo Garcı́a-Sastre, et al.. (2012). Influenza Research Database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and Other Respiratory Viruses. 6(6). 404–416. 231 indexed citations
3.
Pickett, Brett E, Douglas S. Greer, Yun Zhang, et al.. (2012). Virus Pathogen Database and Analysis Resource (ViPR): A Comprehensive Bioinformatics Database and Analysis Resource for the Coronavirus Research Community. Viruses. 4(11). 3209–3226. 118 indexed citations
4.
Pickett, Brett E, Yun Zhang, R. Burke Squires, et al.. (2011). ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Research. 40(D1). D593–D598. 482 indexed citations breakdown →
5.
Zaremba, Sam, Thomas H. Hampton, J. M. Greene, et al.. (2009). Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens. BMC Bioinformatics. 10(1). 177–177. 16 indexed citations
6.
Wang, Siyun, Kaiping Deng, Sam Zaremba, et al.. (2009). Transcriptomic Response of Escherichia coli O157:H7 to Oxidative Stress. Applied and Environmental Microbiology. 75(19). 6110–6123. 134 indexed citations
8.
Correale, Pierpaolo, K. Walmsley, Sam Zaremba, et al.. (1998). Generation of Human Cytolytic T Lymphocyte Lines Directed Against Prostate-Specific Antigen (PSA) Employing a PSA Oligoepitope Peptide. The Journal of Immunology. 161(6). 3186–3194. 110 indexed citations
9.
Schlom, Jeffrey, Kwong-Yok Tsang, Judith A. Kantor, et al.. (1998). Cancer vaccine development. Expert Opinion on Investigational Drugs. 7(9). 1439–1452. 2 indexed citations
10.
Abrams, Scott I., Stephen F. Stanziale, Scott D. Lunin, Sam Zaremba, & Jeffrey Schlom. (1996). Identification of overlapping epitopes in mutant ras oncogene peptides that activate CD4+ and CD8+ T cell responses. European Journal of Immunology. 26(2). 435–443. 46 indexed citations
11.
Abrams, Scott I., Mark J. Dobrzanski, Stephen F. Stanziale, et al.. (1995). Peptide‐specific activation of cytolytic CD4+ T lymphocytes against tumor cells bearing mutated epitopes of K‐ras p21. European Journal of Immunology. 25(9). 2588–2597. 37 indexed citations
12.
Hand, Patricia Horan, et al.. (1993). Biologic properties of a Ch2 domain‐deleted recombinant immunoglobulin. International Journal of Cancer. 53(1). 97–103. 34 indexed citations
14.
Friedman, Beth, Sam Zaremba, & Susan Hockfield. (1990). Monoclonal antibody rat 401 recognizes schwann cells in mature and developing peripheral nerve. The Journal of Comparative Neurology. 295(1). 43–51. 57 indexed citations
15.
Zaremba, Sam, et al.. (1989). Characterization of an activity-dependent, neuronal surface proteoglycan identified with monoclonal antibody Cat-301. Neuron. 2(3). 1207–1219. 168 indexed citations
16.
Angeletti, Ruth Hogue, Judith A. Nolan, & Sam Zaremba. (1985). Catecholamine storage vesicles: topography and function. Trends in Biochemical Sciences. 10(6). 240–243. 2 indexed citations
17.
Zaremba, Sam & Ruth Hogue‐Angeletti. (1985). A reliable method for assessing topographical arrangement of proteins in the chromaffin granule membrane. Neurochemical Research. 10(1). 19–32. 3 indexed citations
18.
Zaremba, Sam & James H. Keen. (1985). Limited proteolytic digestion of coated vesicle assembly polypeptides abolishes reassembly activity. Journal of Cellular Biochemistry. 28(1). 47–58. 51 indexed citations
19.
Zaremba, Sam & Ruth Hogue‐Angeletti. (1982). NADH: (acceptor) oxidoreductase from bovine adrenal medulla chromaffin granules. Archives of Biochemistry and Biophysics. 219(2). 297–305. 6 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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