Mark A. Reddish

3.0k total citations
32 papers, 2.4k citations indexed

About

Mark A. Reddish is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Mark A. Reddish has authored 32 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 18 papers in Radiology, Nuclear Medicine and Imaging and 18 papers in Immunology. Recurrent topics in Mark A. Reddish's work include Glycosylation and Glycoproteins Research (19 papers), Monoclonal and Polyclonal Antibodies Research (18 papers) and Immunotherapy and Immune Responses (15 papers). Mark A. Reddish is often cited by papers focused on Glycosylation and Glycoproteins Research (19 papers), Monoclonal and Polyclonal Antibodies Research (18 papers) and Immunotherapy and Immune Responses (15 papers). Mark A. Reddish collaborates with scholars based in Canada, United States and India. Mark A. Reddish's co-authors include B. Michael Longenecker, Grant D. MacLean, R. Rao Koganty, Mark J. Krantz, Babita Agrawal, James B. Dale, Steven D. Stroop, W. Kyle Simmons, Patrick S.F. Bellgowan and Alex Martin and has published in prestigious journals such as JAMA, Nature Medicine and The Journal of Immunology.

In The Last Decade

Mark A. Reddish

32 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark A. Reddish Canada 25 1.1k 1.1k 592 578 521 32 2.4k
Jean‐Michel Heard France 26 1.9k 1.7× 1.4k 1.2× 131 0.2× 153 0.3× 585 1.1× 52 4.5k
J L Greenstein United States 25 1.6k 1.4× 1.3k 1.2× 498 0.8× 421 0.7× 129 0.2× 47 4.1k
Paul R. Gorry Australia 39 1.7k 1.5× 991 0.9× 120 0.2× 482 0.8× 2.1k 4.1× 120 5.0k
Roberto Manservigi Italy 26 405 0.4× 795 0.7× 114 0.2× 71 0.1× 196 0.4× 92 2.3k
Nicholas J. MacDonald United States 26 435 0.4× 1.0k 0.9× 46 0.1× 600 1.0× 45 0.1× 47 1.9k
Robert Kammerer Germany 26 764 0.7× 596 0.5× 477 0.8× 42 0.1× 84 0.2× 62 2.0k
Xiaoliu Zhang United States 28 526 0.5× 924 0.8× 150 0.3× 42 0.1× 237 0.5× 103 2.4k
Mary Jane Potash United States 26 671 0.6× 693 0.6× 161 0.3× 129 0.2× 523 1.0× 62 2.1k
Yoshikazu Shimomura Japan 31 212 0.2× 722 0.6× 1.5k 2.6× 806 1.4× 65 0.1× 184 3.5k
Manley Huang United States 17 692 0.6× 1.1k 1.0× 158 0.3× 57 0.1× 147 0.3× 28 2.1k

Countries citing papers authored by Mark A. Reddish

Since Specialization
Citations

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

Fields of papers citing papers by Mark A. Reddish

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark A. Reddish. 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 Mark A. Reddish. The network helps show where Mark A. Reddish may publish in the future.

Co-authorship network of co-authors of Mark A. Reddish

This figure shows the co-authorship network connecting the top 25 collaborators of Mark A. Reddish. A scholar is included among the top collaborators of Mark A. Reddish 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 Mark A. Reddish. Mark A. Reddish 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.
Simmons, W. Kyle, Mark A. Reddish, Patrick S.F. Bellgowan, & Alex Martin. (2009). The Selectivity and Functional Connectivity of the Anterior Temporal Lobes. Cerebral Cortex. 20(4). 813–825. 184 indexed citations
2.
McNeil, Shelly, Scott A. Halperin, Joanne M. Langley, et al.. (2005). Safety and Immunogenicity of 26-Valent Group A Streptococcus Vaccine in Healthy Adult Volunteers. Clinical Infectious Diseases. 41(8). 1114–1122. 232 indexed citations
3.
Kotloff, Karen L., Mary C. Corretti, Kathleen Palmer, et al.. (2004). Safety and Immunogenicity of a Recombinant Multivalent Group A Streptococcal Vaccine in Healthy Adults. JAMA. 292(6). 709–709. 126 indexed citations
6.
Agrawal, Babita, Mark J. Krantz, Mark A. Reddish, & B. Michael Longenecker. (1998). Cancer-associated MUC1 mucin inhibits human T-cell proliferation, which is reversible by IL-2. Nature Medicine. 4(1). 43–49. 252 indexed citations
7.
Reddish, Mark A., Grant D. MacLean, R. Rao Koganty, et al.. (1998). Anti-MUC1 class I restricted CTLs in metastatic breast cancer patients immunized with a synthetic MUC1 peptide. International Journal of Cancer. 76(6). 817–823. 108 indexed citations
8.
Agrawal, Babita, Mark J. Krantz, Mark A. Reddish, & B. Michael Longenecker. (1998). Rapid induction of primary human CD4+ and CD8+ T cell responses against cancer-associated MUC1 peptide epitopes.. International Immunology. 10(12). 1907–1916. 47 indexed citations
10.
Ogata, Shunichiro, R. Rao Koganty, Mark A. Reddish, et al.. (1998). Different modes of sialyl-Tn expression during malignant transformation of human colonic mucosa. Glycoconjugate Journal. 15(1). 29–35. 58 indexed citations
11.
Samuel, John, W Budzyński, Mark A. Reddish, et al.. (1998). Immunogenicity and antitumor activity of a liposomal MUC1 peptide-based vaccine. International Journal of Cancer. 75(2). 295–302. 69 indexed citations
12.
Reddish, Mark A., Grant D. MacLean, Sibrand Poppema, Amy L. Berg, & B. Michael Longenecker. (1996). Pre-immunotherapy serum CA27.29 (MUC-1) mucin level and CD69 + lymphocytes correlate with effects of Theratope® sialyl-Tn-KLH cancer vaccine in active specific immunotherapy. Cancer Immunology Immunotherapy. 42(5). 303–309. 71 indexed citations
13.
Agrawal, Babita, Mark A. Reddish, & B. Michael Longenecker. (1996). In vitro induction of MUC-1 peptide-specific type 1 T lymphocyte and cytotoxic T lymphocyte responses from healthy multiparous donors. The Journal of Immunology. 157(5). 2089–2095. 56 indexed citations
14.
MacLean, Grant D., Mark A. Reddish, R. Rao Koganty, & B. Michael Longenecker. (1996). Antibodies Against Mucin-Associated Sialyl-Tn Epitopes Correlate with Survival of Metastatic Adenocarcinoma Patients Undergoing Active Specific Immunotherapy with Synthetic STn Vaccine. Journal of Immunotherapy. 19(1). 59–68. 148 indexed citations
15.
MacLean, Grant D., D.W. Miles, R.D. Rubens, Mark A. Reddish, & B. Michael Longenecker. (1996). Enhancing the Effect of THERATOPE STn-KLH Cancer Vaccine in Patients with Metastatic Breast Cancer by Pretreatment with Low-Dose Intravenous Cyclophosphamide. Journal of Immunotherapy. 19(4). 309–309. 126 indexed citations
16.
Sejbal, Jan, George Kotovych, R. Rao Koganty, et al.. (1995). Structurally defined synthetic cancer vaccines: analysis of structure, glycosylation and recognition of cancer associated mucin, MUC-1 derived peptides. Glycoconjugate Journal. 12(5). 607–617. 29 indexed citations
18.
Longenecker, B. Michael, Mark A. Reddish, R. Rao Koganty, & Grant D. MacLean. (1994). Specificity of the IgG Response in Mice and Human Breast Cancer Patients Following Immunization Against Synthetic Sialyl-Tn, An Epitope with Possible Functional Significance in Metastasis. Advances in experimental medicine and biology. 353. 105–124. 29 indexed citations
19.
MacLean, Grant D., Mark A. Reddish, Mary Beth Bowen-Yacyshyn, Sibrand Poppema, & B. Michael Longenecker. (1994). Active Specific Immunotherapy Against Adenocarcinomas. Cancer Investigation. 12(1). 46–56. 42 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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