Sunil K. Chatterjee

1.8k citations
58 papers · 1.5k · h-index 22

Impact in

Papers in

    • Glycosylation and Glycoproteins Research 22
    • Immunotherapy and Immune Responses 19
    • T-cell and B-cell Immunology 7
    • Galectins and Cancer Biology 4

Sunil K. Chatterjee

57 papers receiving 1.4k citations

Peers

Sunil K. Chatterjee
Comparison fields: 5 of 85
  • Immunology 644
  • Radiology, Nuclear Medicine and Imaging 346
  • Biotechnology 141
  • Oncology 403
  • Molecular Biology 760
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Citations per field
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Citations per year

Countries citing papers authored by Sunil K. Chatterjee

Since Specialization
Citations

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

Fields of papers citing papers by Sunil K. Chatterjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunil K. Chatterjee. 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 Sunil K. Chatterjee. The network helps show where Sunil K. Chatterjee may publish in the future.

Co-authors

The 25 scholars most cited alongside Sunil K. Chatterjee, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sunil K. Chatterjee Line = papers co-authored together Sunil K. Chatterjee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Glycosyltransferase and glycosidase activities in ovarian cancer patients.
1979149
2 2005129
3 1999123
4
Monoclonal antibodies recognizing tumor-associated antigen of human ovarian mucinous cystadenocarcinomas.
198261
5 197854
6 200049
7 199648
8
Uridine 5'-diphosphate-galactose:glycoprotein galactosyltransferase activity in the ovarian cancer patient.
197646
9 197745
10 199142
11 200140
12 200536
13 200034
14 200432
15 200532
16 200228
17 200727
18 200324
19 200524
20 200022

About Sunil K. Chatterjee

Sunil K. Chatterjee is a scholar working on Molecular Biology, Immunology, Radiology, Nuclear Medicine and Imaging, Oncology and Physiology, having authored 58 papers that have together received 1.5k indexed citations. Recurring topics across this work include Glycosylation and Glycoproteins Research (22 papers), Immunotherapy and Immune Responses (19 papers), Monoclonal and Polyclonal Antibodies Research (15 papers), T-cell and B-cell Immunology (7 papers), Adenosine and Purinergic Signaling (6 papers), Cancer Research and Treatments (5 papers), Galectins and Cancer Biology (4 papers) and Carbohydrate Chemistry and Synthesis (4 papers). The work is most often cited by research in Immunology (644 citations), Radiology, Nuclear Medicine and Imaging (346 citations), Biotechnology (141 citations), Oncology (403 citations) and Molecular Biology (760 citations). Sunil K. Chatterjee has collaborated with scholars based in United States, India and Malaysia. Frequent co-authors include Malaya Bhattacharya‐Chatterjee, Joseph J. Barlow, Malaya Bhattacharya, Kenneth A. Foon, Untae Kim, Santanu Dasgupta, Bert W. O’Malley, Asim Saha, Pulak Tripathi and Mala Chakraborty. Their work appears in journals such as JNCI Journal of the National Cancer Institute, Cancer Immunology Immunotherapy, Cancer Research, Molecular Therapy and Cancer Letters.

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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