Yaoli Song

5.3k total citations · 1 hit paper
7 papers, 3.5k citations indexed

About

Yaoli Song is a scholar working on Immunology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Yaoli Song has authored 7 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Immunology, 3 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Molecular Biology. Recurrent topics in Yaoli Song's work include Monoclonal and Polyclonal Antibodies Research (3 papers), Immune Cell Function and Interaction (3 papers) and Neuroinflammation and Neurodegeneration Mechanisms (2 papers). Yaoli Song is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (3 papers), Immune Cell Function and Interaction (3 papers) and Neuroinflammation and Neurodegeneration Mechanisms (2 papers). Yaoli Song collaborates with scholars based in United States and United Kingdom. Yaoli Song's co-authors include Daniel M. Gorman, Mehrdad Moshrefi, Robert A. Kastelein, Elizabeth R. Oldham, J. Fernando Bazán, Xiaoxia Li, Jochen Schmitz, Erin Murphy, Gérard Zurawski and Jinzhong Qin and has published in prestigious journals such as Immunity, The Journal of Immunology and Analytical Chemistry.

In The Last Decade

Yaoli Song

7 papers receiving 3.4k citations

Hit Papers

IL-33, an Interleukin-1-like Cytokine that Signals via th... 2005 2026 2012 2019 2005 500 1000 1.5k 2.0k 2.5k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaoli Song United States 6 2.9k 1.3k 677 402 239 7 3.5k
Mehrdad Moshrefi United States 3 2.6k 0.9× 1.3k 1.0× 650 1.0× 325 0.8× 67 0.3× 3 3.1k
Alexander M. Owyang United States 9 3.3k 1.2× 1.4k 1.1× 768 1.1× 463 1.2× 57 0.2× 12 4.1k
Emily Hams Ireland 30 2.6k 0.9× 1.2k 0.9× 655 1.0× 654 1.6× 60 0.3× 43 4.0k
Emily Thornton United States 14 1.9k 0.7× 975 0.7× 468 0.7× 420 1.0× 51 0.2× 16 3.1k
Karel Otero United States 22 2.4k 0.8× 593 0.5× 283 0.4× 584 1.5× 476 2.0× 27 3.3k
Elisabeth Suri‐Payer Germany 34 4.2k 1.5× 675 0.5× 267 0.4× 498 1.2× 747 3.1× 39 5.5k
Jennifer A. Walker United Kingdom 20 3.5k 1.2× 1.8k 1.4× 598 0.9× 710 1.8× 36 0.2× 27 4.5k
Nader Najafian United States 40 3.3k 1.2× 942 0.7× 238 0.4× 699 1.7× 88 0.4× 96 5.3k
Ola Winqvist Sweden 40 2.1k 0.7× 598 0.5× 408 0.6× 932 2.3× 64 0.3× 128 4.6k
Tom Cupedo Netherlands 36 4.3k 1.5× 1.6k 1.2× 560 0.8× 841 2.1× 35 0.1× 74 5.6k

Countries citing papers authored by Yaoli Song

Since Specialization
Citations

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

Fields of papers citing papers by Yaoli Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaoli Song

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

All Works

7 of 7 papers shown
1.
Tabrizi, Mohammad, Harish Shankaran, Veronica Juan, et al.. (2019). Pharmacokinetic Properties of Humanized IgG1 and IgG4 Antibodies in Preclinical Species: Translational Evaluation. The AAPS Journal. 21(3). 39–39. 10 indexed citations
2.
Gorman, Daniel M., Ming‐Tain Lai, Carolyn McHale, et al.. (2019). Treatment of SHIV-infected, ART-suppressed rhesus macaques with bispecific HIVxCD3 DART® molecules. Journal of Virus Eradication. 5. 22–22. 1 indexed citations
3.
Zhang, Qian, Daniel S. Spellman, Yaoli Song, et al.. (2014). Generic Automated Method for Liquid Chromatography–Multiple Reaction Monitoring Mass Spectrometry Based Monoclonal Antibody Quantitation for Preclinical Pharmacokinetic Studies. Analytical Chemistry. 86(17). 8776–8784. 43 indexed citations
4.
Alvarez, Hamsell M., On‐Yee So, Huiping Ma, et al.. (2011). Effects of PEGylation and Immune Complex Formation on the Pharmacokinetics and Biodistribution of Recombinant Interleukin 10 in Mice. Drug Metabolism and Disposition. 40(2). 360–373. 18 indexed citations
5.
Cherwinski, Holly, Craig A. Murphy, Mike Bigler, et al.. (2005). The CD200 Receptor Is a Novel and Potent Regulator of Murine and Human Mast Cell Function. The Journal of Immunology. 174(3). 1348–1356. 95 indexed citations
6.
Schmitz, Jochen, Alexander M. Owyang, Elizabeth R. Oldham, et al.. (2005). IL-33, an Interleukin-1-like Cytokine that Signals via the IL-1 Receptor-Related Protein ST2 and Induces T Helper Type 2-Associated Cytokines. Immunity. 23(5). 479–490. 2970 indexed citations breakdown →
7.
Wright, Gavin J., Holly Cherwinski, Mildred Foster‐Cuevas, et al.. (2003). Characterization of the CD200 Receptor Family in Mice and Humans and Their Interactions with CD200. The Journal of Immunology. 171(6). 3034–3046. 372 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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