Robert Abbott

7.7k total citations · 1 hit paper
44 papers, 2.5k citations indexed

About

Robert Abbott is a scholar working on Immunology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Robert Abbott has authored 44 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Immunology, 8 papers in Artificial Intelligence and 7 papers in Computer Networks and Communications. Recurrent topics in Robert Abbott's work include Immune Cell Function and Interaction (8 papers), Distributed systems and fault tolerance (6 papers) and Adenosine and Purinergic Signaling (5 papers). Robert Abbott is often cited by papers focused on Immune Cell Function and Interaction (8 papers), Distributed systems and fault tolerance (6 papers) and Adenosine and Purinergic Signaling (5 papers). Robert Abbott collaborates with scholars based in United States, United Kingdom and Mexico. Robert Abbott's co-authors include Héctor García-Molina, Michail V. Sitkovsky, Stephen Hatfield, Shane Crotty, Bryan Belikoff, Dmitriy Lukashev, Phaethon Philbrook, Shalini Sethumadhavan, William R. Schief and Akio Ohta and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Immunity.

In The Last Decade

Robert Abbott

40 papers receiving 2.3k citations

Hit Papers

Immunological mechanisms of the antitumor effects of supp... 2015 2026 2018 2022 2015 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
Robert Abbott United States 19 787 786 562 408 335 44 2.5k
Étienne Gagnon Canada 23 315 0.4× 1.7k 2.2× 229 0.4× 1.4k 3.5× 352 1.1× 42 4.4k
Yiping Fan United States 37 349 0.4× 1.9k 2.4× 536 1.0× 1.8k 4.3× 1.4k 4.2× 97 5.4k
Xiaoying Jia United States 26 399 0.5× 314 0.4× 38 0.1× 1.3k 3.2× 349 1.0× 83 2.8k
Robert Carter United States 20 788 1.0× 2.0k 2.6× 41 0.1× 1.6k 4.0× 799 2.4× 43 4.7k
Jacob Appelbaum United States 16 257 0.3× 391 0.5× 198 0.4× 588 1.4× 252 0.8× 41 2.1k
Ion Măndoiu United States 24 297 0.4× 445 0.6× 186 0.3× 1.1k 2.6× 283 0.8× 140 2.2k
Wei Jin China 29 104 0.1× 309 0.4× 25 0.0× 1.3k 3.3× 232 0.7× 75 2.6k
Werner Haas Germany 26 173 0.2× 2.6k 3.3× 163 0.3× 560 1.4× 636 1.9× 81 3.8k
Michael D. Bond United States 30 874 1.1× 114 0.1× 822 1.5× 596 1.5× 134 0.4× 88 2.3k
John Paul Shen United States 30 1.1k 1.4× 96 0.1× 1.3k 2.4× 1.4k 3.5× 559 1.7× 162 3.9k

Countries citing papers authored by Robert Abbott

Since Specialization
Citations

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

Fields of papers citing papers by Robert Abbott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Abbott

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Abbott. A scholar is included among the top collaborators of Robert Abbott 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 Robert Abbott. Robert Abbott 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.
Stewart, C. L., et al.. (2025). Machine learning for reactor power monitoring with limited labeled data. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 1073. 170285–170285.
2.
Amoozgar, Behzad, Elizabeth Simms, Daniel Eliáš, et al.. (2025). Exploring the gut microbiome’s influence on cancer-associated anemia: Mechanisms, clinical challenges, and innovative therapies. World Journal of Gastrointestinal Pharmacology and Therapeutics. 16(2). 105375–105375. 3 indexed citations
3.
Pruitt, Layne & Robert Abbott. (2024). Hypoxia-adenosinergic regulation of B cell responses. Frontiers in Immunology. 15. 1478506–1478506. 1 indexed citations
4.
Kuzmin, Ivan V., Layne Pruitt, Chad E. Mire, et al.. (2024). Comparison of uridine and N1-methylpseudouridine mRNA platforms in development of an Andes virus vaccine. Nature Communications. 15(1). 6421–6421. 6 indexed citations
6.
Lee, Jeong Hyun, Catherine Nakao, Michael Y. Appel, et al.. (2022). Highly mutated antibodies capable of neutralizing N276 glycan-deficient HIV after a single immunization with an Env trimer. Cell Reports. 38(10). 110485–110485. 2 indexed citations
7.
Kato, Yu, Robert Abbott, Sonya Haupt, et al.. (2020). Multifaceted Effects of Antigen Valency on B Cell Response Composition and Differentiation In Vivo. Immunity. 53(3). 548–563.e8. 135 indexed citations
8.
Havenar‐Daughton, Colin, Robert Abbott, William R. Schief, & Shane Crotty. (2018). When designing vaccines, consider the starting material: the human B cell repertoire. Current Opinion in Immunology. 53. 209–216. 46 indexed citations
9.
Abbott, Robert, Jeong Hyun Lee, Sergey Menis, et al.. (2017). Precursor Frequency and Affinity Determine B Cell Competitive Fitness in Germinal Centers, Tested with Germline-Targeting HIV Vaccine Immunogens. Immunity. 48(1). 133–146.e6. 204 indexed citations
10.
Silva, Murillo, Thao H. Nguyen, Phaethon Philbrook, et al.. (2017). Targeted Elimination of Immunodominant B Cells Drives the Germinal Center Reaction toward Subdominant Epitopes. Cell Reports. 21(13). 3672–3680. 22 indexed citations
11.
Abbott, Robert, Murillo Silva, Derek W. Cain, et al.. (2017). The GS Protein-coupled A2a Adenosine Receptor Controls T Cell Help in the Germinal Center. Journal of Biological Chemistry. 292(4). 1211–1217. 25 indexed citations
12.
Hatfield, Stephen, Jørgen Kjaergäard, Dmitriy Lukashev, et al.. (2014). Systemic oxygenation weakens the hypoxia and hypoxia inducible factor 1α-dependent and extracellular adenosine-mediated tumor protection. Journal of Molecular Medicine. 92(12). 1283–1292. 165 indexed citations
13.
Sitkovsky, Michail V., Stephen Hatfield, Robert Abbott, et al.. (2014). Hostile, Hypoxia–A2-Adenosinergic Tumor Biology as the Next Barrier to Overcome for Tumor Immunologists. Cancer Immunology Research. 2(7). 598–605. 174 indexed citations
14.
Morgan, Brent, et al.. (2013). Individual Differences in Multitasking Ability and Adaptability. Human Factors The Journal of the Human Factors and Ergonomics Society. 55(4). 776–788. 55 indexed citations
15.
Abbott, Robert. (2007). Automated tactics modeling: techniques and applications. 185–185. 2 indexed citations
16.
Abbott, Robert. (1997). Information transfer and cognitive mismatch: a Popperian model for studies of public understanding. Journal of Information Science. 23(2). 129–137. 4 indexed citations
17.
Abbott, Robert & Héctor García-Molina. (1989). Scheduling real-time transactions with disk resident data. Very Large Data Bases. 385–395. 110 indexed citations
18.
Abbott, Robert & Héctor García-Molina. (1988). Scheduling Real-time Transactions: a Performance Evaluation. Very Large Data Bases. 1–12. 74 indexed citations
19.
García-Molina, Héctor & Robert Abbott. (1987). Reliable distributed database management. Proceedings of the IEEE. 75(5). 601–620. 31 indexed citations
20.
Abbott, Robert, M. Anbar, Howard Faden, et al.. (1980). Diagnosis of viral infections by multicomponent mass spectrometric analysis.. Clinical Chemistry. 26(10). 1443–1449. 12 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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