Ramesh Baliga

1.2k total citations
17 papers, 378 citations indexed

About

Ramesh Baliga is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Immunology. According to data from OpenAlex, Ramesh Baliga has authored 17 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Molecular Biology and 7 papers in Immunology. Recurrent topics in Ramesh Baliga's work include Monoclonal and Polyclonal Antibodies Research (12 papers), Glycosylation and Glycoproteins Research (4 papers) and Immunotherapy and Immune Responses (4 papers). Ramesh Baliga is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (12 papers), Glycosylation and Glycoproteins Research (4 papers) and Immunotherapy and Immune Responses (4 papers). Ramesh Baliga collaborates with scholars based in United States and France. Ramesh Baliga's co-authors include Donald M. Crothers, Bruce A. Keyt, Marvin S. Peterson, Stephen F. Carroll, Angus M. Sinclair, Christopher J L Murray, Eldon E. Baird, David Herman, Peter B. Dervan and Christian Melander and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Clinical Oncology.

In The Last Decade

Ramesh Baliga

15 papers receiving 355 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ramesh Baliga United States	9	266	151	71	46	30	17	378
Hung‐Pin Peng Taiwan	15	403 1.5×	257 1.7×	87 1.2×	45 1.0×	21 0.7×	27	614
Nicolas Aubrey France	16	341 1.3×	216 1.4×	103 1.5×	69 1.5×	15 0.5×	44	603
Charles Zhu United States	11	241 0.9×	149 1.0×	37 0.5×	49 1.1×	29 1.0×	13	508
Jonas Kügler Germany	12	265 1.0×	246 1.6×	75 1.1×	48 1.0×	53 1.8×	17	436
Alexander Klimka Germany	11	208 0.8×	192 1.3×	147 2.1×	57 1.2×	67 2.2×	16	413
Kong Meng Hoi Singapore	7	235 0.9×	128 0.8×	35 0.5×	38 0.8×	22 0.7×	8	300
Yoan Machado Canada	13	210 0.8×	53 0.4×	119 1.7×	29 0.6×	24 0.8×	21	459
Violaine Moreau France	11	255 1.0×	156 1.0×	61 0.9×	16 0.3×	9 0.3×	17	321
Karin Ahrer Austria	11	393 1.5×	173 1.1×	44 0.6×	14 0.3×	23 0.8×	12	480
James E. Stray United States	13	321 1.2×	175 1.2×	39 0.5×	35 0.8×	9 0.3×	20	515

Countries citing papers authored by Ramesh Baliga

Since Specialization

Citations

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

Fields of papers citing papers by Ramesh Baliga

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramesh Baliga

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

All Works

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17 of 17 papers shown

1.

Hart, Kevin C., Paul R. Hinton, Sandra M. Schneider, et al.. (2025). An engineered IgM antibody targeting CD20 has enhanced complement-dependent cytotoxicity compared with an IgG. Experimental Hematology. 152. 105250–105250.

2.

Kotturi, Maya F., Paul R. Hinton, Kathryn Logronio, et al.. (2021). 841P IGM-2323 is a CD20xCD3 IgM bispecific T-cell engager that kills low CD20-expressing and rituximab-resistant B-cell lymphomas. Annals of Oncology. 32. S778–S779. 1 indexed citations

3.

Baliga, Ramesh, Keyu Li, Paul R. Hinton, et al.. (2020). Abstract 5664: A bispecific IgM antibody format for enhanced T cell dependent killing with minimal cytokine release. Cancer Research. 80(16_Supplement). 5664–5664. 3 indexed citations

4.

Wang, Béatrice, Ling Wang, Bing Shan, et al.. (2020). Abstract 518: Agonistic Death Receptor 5 (DR5) IgM antibody IGM-8444 induces tumor cell apoptosis in vitro and in vivo and has a favorable in vitro safety profile. Cancer Research. 80(16_Supplement). 518–518. 1 indexed citations

5.

Keyt, Bruce A., Ramesh Baliga, Keyu Li, et al.. (2020). Lymphoma cell-killing activity and cytokine release by CD20-directed bispecific IgM antibody-based T-cell engager (IGM-2323).. Journal of Clinical Oncology. 38(15_suppl). e15007–e15007. 1 indexed citations

6.

Keyt, Bruce A., Ramesh Baliga, Angus M. Sinclair, Stephen F. Carroll, & Marvin S. Peterson. (2020). Structure, Function, and Therapeutic Use of IgM Antibodies. SHILAP Revista de lepidopterología. 9(4). 53–53. 134 indexed citations

7.

Wang, Béatrice, Ling Wang, Avneesh K. Saini, et al.. (2019). Abstract 3050: Multimeric IgM antibodies targeting DR5 are potent and rapid inducers of tumor cell apoptosis and cell death in vitro and in vivo. Cancer Research. 79(13_Supplement). 3050–3050. 2 indexed citations

8.

Wang, Béatrice, Ling Wang, Avneesh K. Saini, et al.. (2019). Abstract 3050: Multimeric IgM antibodies targeting DR5 are potent and rapid inducers of tumor cell apoptosis and cell death in vitro and in vivo. 3050–3050. 1 indexed citations

9.

Baliga, Ramesh, Keyu Li, Paul R. Hinton, et al.. (2019). High Avidity IgM-Based CD20xCD3 Bispecific Antibody (IGM-2323) for Enhanced T-Cell Dependent Killing with Minimal Cytokine Release. Blood. 134(Supplement_1). 1574–1574. 12 indexed citations

10.

Wang, Béatrice, Paul R. Hinton, Dean Ng, et al.. (2017). Abstract 1702: Multimeric anti-DR5 IgM antibody displays potent cytotoxicity in vitro and promotes tumor regression in vivo. Cancer Research. 77(13_Supplement). 1702–1702. 3 indexed citations

11.

Stafford, Ryan, M Matsumoto, Gang Yin, et al.. (2014). In vitro Fab display: a cell-free system for IgG discovery. Protein Engineering Design and Selection. 27(4). 97–109. 34 indexed citations

12.

Groff, Dan, Juan Zhang, Junhao Yang, et al.. (2014). Engineering toward a bacterial “endoplasmic reticulum” for the rapid expression of immunoglobulin proteins. mAbs. 6(3). 671–678. 51 indexed citations

13.

Murray, Christopher J L & Ramesh Baliga. (2013). Cell-free translation of peptides and proteins:from high throughput screening to clinical production. Current Opinion in Chemical Biology. 17(3). 420–426. 48 indexed citations

14.

Marini, Nicholas J., Ramesh Baliga, Matthew Taylor, et al.. (2003). DNA Binding Hairpin Polyamides with Antifungal Activity. Chemistry & Biology. 10(7). 635–644. 8 indexed citations

15.

Baliga, Ramesh & Donald M. Crothers. (2000). On the kinetics of distamycin binding to its target sites on duplex DNA. Proceedings of the National Academy of Sciences. 97(14). 7814–7818. 34 indexed citations

16.

Baliga, Ramesh & Donald M. Crothers. (2000). The Kinetic Basis for Sequence Discrimination by Distamycin A. Journal of the American Chemical Society. 122(47). 11751–11752. 12 indexed citations

17.

Baliga, Ramesh, Eldon E. Baird, David Herman, et al.. (2000). Kinetic Consequences of Covalent Linkage of DNA Binding Polyamides. Biochemistry. 40(1). 3–8. 33 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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