Ling Tong

2.5k total citations · 2 hit papers
11 papers, 1.9k citations indexed

About

Ling Tong is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Ling Tong has authored 11 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Cancer Research and 3 papers in Oncology. Recurrent topics in Ling Tong's work include Cancer, Lipids, and Metabolism (3 papers), Nanoparticle-Based Drug Delivery (2 papers) and CAR-T cell therapy research (2 papers). Ling Tong is often cited by papers focused on Cancer, Lipids, and Metabolism (3 papers), Nanoparticle-Based Drug Delivery (2 papers) and CAR-T cell therapy research (2 papers). Ling Tong collaborates with scholars based in United States, Germany and United Kingdom. Ling Tong's co-authors include Dean W. Felsher, Arvin M. Gouw, Virginie Baylot, Kelly N. Fitzgerald, Yulin Li, Susanne Walz, Martin Eilers, Ines Gütgemann, Stephanie C. Casey and Grigory Tikhomirov and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Ling Tong

11 papers receiving 1.9k citations

Hit Papers

MYC regulates the antitum... 2014 2026 2018 2022 2016 2014 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ling Tong United States 8 871 730 595 367 362 11 1.9k
Michael J. Ciesielski United States 30 1.1k 1.3× 648 0.9× 742 1.2× 259 0.7× 308 0.9× 69 2.4k
Barbara Muz United States 22 1.3k 1.5× 770 1.1× 484 0.8× 511 1.4× 913 2.5× 58 2.8k
Jianguo Wang China 21 1.1k 1.2× 606 0.8× 605 1.0× 311 0.8× 496 1.4× 49 2.0k
G‐One Ahn South Korea 24 838 1.0× 868 1.2× 666 1.1× 337 0.9× 455 1.3× 44 2.4k
Pilar de la Puente United States 22 1.2k 1.3× 717 1.0× 324 0.5× 609 1.7× 855 2.4× 50 2.7k
Yiting Qiao China 21 867 1.0× 474 0.6× 280 0.5× 558 1.5× 261 0.7× 38 1.9k
Quang‐Dé Nguyen United Kingdom 25 1.0k 1.2× 642 0.9× 267 0.4× 215 0.6× 355 1.0× 53 2.1k
Mahaveer S. Bhojani United States 23 1.4k 1.6× 749 1.0× 497 0.8× 735 2.0× 305 0.8× 37 2.8k
Allison S. Harney United States 16 735 0.8× 957 1.3× 641 1.1× 276 0.8× 362 1.0× 26 1.9k
Karolien Castermans Belgium 24 1.1k 1.3× 449 0.6× 378 0.6× 184 0.5× 425 1.2× 31 2.1k

Countries citing papers authored by Ling Tong

Since Specialization
Citations

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

Fields of papers citing papers by Ling Tong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Tong

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

All Works

11 of 11 papers shown
1.
Deutzmann, Anja, Delaney K. Sullivan, Renumathy Dhanasekaran, et al.. (2024). Nuclear to cytoplasmic transport is a druggable dependency in MYC-driven hepatocellular carcinoma. Nature Communications. 15(1). 963–963. 8 indexed citations
2.
Gouw, Arvin M., Vineet Kumar, Katherine Margulis, et al.. (2022). Azapodophyllotoxin Causes Lymphoma and Kidney Cancer Regression by Disrupting Tubulin and Monoglycerols. ACS Medicinal Chemistry Letters. 13(4). 615–622. 1 indexed citations
3.
4.
Hori, Sharon S., Ling Tong, Srividya Swaminathan, et al.. (2021). A mathematical model of tumor regression and recurrence after therapeutic oncogene inactivation. Scientific Reports. 11(1). 1341–1341. 7 indexed citations
5.
Dhanasekaran, Renumathy, Virginie Baylot, Sibu Kuruvilla, et al.. (2020). MYC and Twist1 cooperate to drive metastasis by eliciting crosstalk between cancer and innate immunity. eLife. 9. 46 indexed citations
6.
Gouw, Arvin M., Katherine Margulis, Anthony Mancuso, et al.. (2019). The MYC Oncogene Cooperates with Sterol-Regulated Element-Binding Protein to Regulate Lipogenesis Essential for Neoplastic Growth. Cell Metabolism. 30(3). 556–572.e5. 146 indexed citations
7.
Gouw, Arvin M., Lívia S. Eberlin, Katherine Margulis, et al.. (2017). Oncogene KRAS activates fatty acid synthase, resulting in specific ERK and lipid signatures associated with lung adenocarcinoma. Proceedings of the National Academy of Sciences. 114(17). 4300–4305. 123 indexed citations
8.
Casey, Stephanie C., Ling Tong, Yulin Li, et al.. (2016). MYC regulates the antitumor immune response through CD47 and PD-L1. Science. 352(6282). 227–231. 1018 indexed citations breakdown →
9.
Ye, Deju, Adam J. Shuhendler, Lina Cui, et al.. (2014). Bioorthogonal cyclization-mediated in situ self-assembly of small-molecule probes for imaging caspase activity in vivo. Nature Chemistry. 6(6). 519–526. 425 indexed citations breakdown →
11.

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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