Araz Eivazi

1.4k total citations · 1 hit paper
7 papers, 873 citations indexed

About

Araz Eivazi is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Araz Eivazi has authored 7 papers receiving a total of 873 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Oncology. Recurrent topics in Araz Eivazi's work include Monoclonal and Polyclonal Antibodies Research (5 papers), Glycosylation and Glycoproteins Research (3 papers) and CAR-T cell therapy research (3 papers). Araz Eivazi is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (5 papers), Glycosylation and Glycoproteins Research (3 papers) and CAR-T cell therapy research (3 papers). Araz Eivazi collaborates with scholars based in Morocco and France. Araz Eivazi's co-authors include Sher Karki, Greg A. Lazar, Wei Dang, Jost Vielmetter, Sean C. Yoder, Cheryl Chan, David F. Carmichael, Omid Vafa, Robert J. Hayes and Helen Chung and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and Cancer Research.

In The Last Decade

Araz Eivazi

7 papers receiving 811 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Engineered antibody Fc variants with enhanced effector function

2006 590 citations Greg A. Lazar, Wei Dang et al. Proceedings of the National Academy of Sciences profile →

Peers

Araz Eivazi

RNMI

MB

IMMUN

ONCOL

GENET

Jaume Pons United States

Inka Scheffrahn Germany

Kathy Mulgrew United States

Mark Tornetta United States

Sonya James United Kingdom

Heidi H. van Ojik Netherlands

Yasushi Yoshimura Japan

Suravi Raychaudhuri United States

Renaud Lesourne France

Alain Vallé France

Jaume Pons United States

Araz Eivazi

337 ×0.6

RNMI

386 ×0.9

MB

405 ×1.1

IMMUN

191 ×0.7

ONCOL

23 ×0.3

GENET

Citations per year, relative to Araz Eivazi Araz Eivazi (= 1×) peers Jaume Pons

Countries citing papers authored by Araz Eivazi

Since Specialization

Citations

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

Fields of papers citing papers by Araz Eivazi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Araz Eivazi

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Moore, Gregory L., Juan E. Diaz, Christine Bonzon, et al.. (2024). A B7-H3–Targeted CD28 Bispecific Antibody Enhances the Activity of Anti–PD-1 and CD3 T-cell Engager Immunotherapies. Molecular Cancer Therapeutics. 24(3). 331–344. 11 indexed citations

2.

Faber, Matthew S., Sung‐Hyung Lee, Yoon Kyung Kim, et al.. (2021). Abstract 1860: Bispecific claudin-6 x CD3 antibodies in a 2 + 1 format demonstrate selectivity and activity on human ovarian cancer cells. Cancer Research. 81(13_Supplement). 1860–1860. 5 indexed citations

3.

Bernett, Matthew J., Ke Liu, Christine Bonzon, et al.. (2021). 707 IL12 Fc-fusions engineered for reduced potency and extended half-life exhibit strong anti-tumor activity and improved therapeutic index compared to wild-type IL12 agents. Regular and Young Investigator Award Abstracts. A736–A736. 1 indexed citations

4.

Moore, Gregory L., Matthew J. Bernett, Rumana Rashid, et al.. (2018). A robust heterodimeric Fc platform engineered for efficient development of bispecific antibodies of multiple formats. Methods. 154. 38–50. 49 indexed citations

5.

Moore, Gregory L., Erik Pong, Duc-Hanh T. Nguyen, et al.. (2011). A novel bispecific antibody format enables simultaneous bivalent and monovalent co-engagement of distinct target antigens. mAbs. 3(6). 546–557. 89 indexed citations

6.

Zalevsky, Jonathan, Thomas Sécher, Sergei A. Ezhevsky, et al.. (2007). Dominant-Negative Inhibitors of Soluble TNF Attenuate Experimental Arthritis without Suppressing Innate Immunity to Infection. The Journal of Immunology. 179(3). 1872–1883. 128 indexed citations

7.

Lazar, Greg A., Wei Dang, Sher Karki, et al.. (2006). Engineered antibody Fc variants with enhanced effector function. Proceedings of the National Academy of Sciences. 103(11). 4005–4010. 590 indexed citations breakdown →

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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