Jasmine Foo

3.1k total citations
50 papers, 1.7k citations indexed

About

Jasmine Foo is a scholar working on Cancer Research, Molecular Biology and Modeling and Simulation. According to data from OpenAlex, Jasmine Foo has authored 50 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cancer Research, 16 papers in Molecular Biology and 15 papers in Modeling and Simulation. Recurrent topics in Jasmine Foo's work include Cancer Genomics and Diagnostics (18 papers), Mathematical Biology Tumor Growth (15 papers) and Evolution and Genetic Dynamics (9 papers). Jasmine Foo is often cited by papers focused on Cancer Genomics and Diagnostics (18 papers), Mathematical Biology Tumor Growth (15 papers) and Evolution and Genetic Dynamics (9 papers). Jasmine Foo collaborates with scholars based in United States, Iceland and France. Jasmine Foo's co-authors include Franziska Michor, George Em Karniadakis, Kevin Leder, Xiaoliang Wan, Shannon M. Mumenthaler, William Pao, Didier Lucor, Rick Durrett, David B. Agus and Parag Mallick and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Cancer Research.

In The Last Decade

Jasmine Foo

50 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jasmine Foo United States	23	530	507	348	331	331	50	1.7k
Marcel Schilling Germany	17	102 0.2×	1.4k 2.7×	179 0.5×	225 0.7×	97 0.3×	31	2.2k
Andrea Hawkins‐Daarud United States	19	183 0.3×	245 0.5×	481 1.4×	182 0.5×	28 0.1×	40	1.2k
Ernesto A. B. F. Lima United States	19	162 0.3×	238 0.5×	520 1.5×	184 0.6×	27 0.1×	45	992
Marzio Pennisi Italy	24	45 0.1×	613 1.2×	240 0.7×	134 0.4×	44 0.1×	77	1.3k
Willi Jäger Germany	26	45 0.1×	499 1.0×	729 2.1×	150 0.5×	24 0.1×	103	3.2k
Tomoko Umeda Japan	16	119 0.2×	139 0.3×	57 0.2×	119 0.4×	29 0.1×	51	1.1k
Pamela Burrage Australia	17	55 0.1×	346 0.7×	156 0.4×	72 0.2×	70 0.2×	59	1.2k
Roberto Serra Italy	20	136 0.3×	684 1.3×	29 0.1×	110 0.3×	7 0.0×	105	1.5k
Florian Markowetz United Kingdom	33	1.6k 3.0×	2.9k 5.7×	132 0.4×	1.4k 4.4×	28 0.1×	94	5.6k

Countries citing papers authored by Jasmine Foo

Since Specialization

Citations

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

Fields of papers citing papers by Jasmine Foo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jasmine Foo

This figure shows the co-authorship network connecting the top 25 collaborators of Jasmine Foo. A scholar is included among the top collaborators of Jasmine Foo 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 Jasmine Foo. Jasmine Foo 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:

20 of 20 papers shown

Lou, Emil, et al.. (2025). Early Circulating Tumor DNA Kinetics as a Dynamic Biomarker of Cancer Treatment Response. JCO Clinical Cancer Informatics. 9(9). e2400160–e2400160. 1 indexed citations

Foo, Jasmine, et al.. (2025). A statistical framework for detecting therapy-induced resistance from drug screens. npj Systems Biology and Applications. 11(1). 88–88. 1 indexed citations

Magnusson, Bengt, et al.. (2025). Optimal dosing of anti-cancer treatment under drug-induced plasticity. npj Systems Biology and Applications. 11(1). 98–98. 1 indexed citations

Kim, Seungil, et al.. (2024). Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework. PLoS Computational Biology. 20(8). e1012256–e1012256. 4 indexed citations

Foo, Jasmine, et al.. (2023). A comparison of mutation and amplification-driven resistance mechanisms and their impacts on tumor recurrence. Journal of Mathematical Biology. 87(4). 59–59. 2 indexed citations

Foo, Jasmine, et al.. (2023). Spread of premalignant mutant clones and cancer initiation in multilayered tissue. The Annals of Applied Probability. 33(1). 1 indexed citations

De, Subhajyoti, et al.. (2020). Understanding the role of phenotypic switching in cancer drug resistance. Journal of Theoretical Biology. 490. 110162–110162. 41 indexed citations

Kim, Seungil, Ren Sun, Nolan Ung, et al.. (2020). Comparison of Cell and Organoid-Level Analysis of Patient-Derived 3D Organoids to Evaluate Tumor Cell Growth Dynamics and Drug Response. SLAS DISCOVERY. 25(7). 744–754. 42 indexed citations

Ryser, Marc D., Walter T. Lee, Neal Ready, Kevin Leder, & Jasmine Foo. (2016). Quantifying the Dynamics of Field Cancerization in Tobacco-Related Head and Neck Cancer: A Multiscale Modeling Approach. Cancer Research. 76(24). 7078–7088. 22 indexed citations

10.

Chiang, Chun-Te, et al.. (2016). A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Scientific Reports. 6(1). 29752–29752. 49 indexed citations

11.

He, Qie, Junfeng Zhu, David Dingli, Jasmine Foo, & Kevin Leder. (2016). Optimized Treatment Schedules for Chronic Myeloid Leukemia. PLoS Computational Biology. 12(10). e1005129–e1005129. 11 indexed citations

12.

Mumenthaler, Shannon M., Jasmine Foo, Kevin Leder, et al.. (2015). The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells. SHILAP Revista de lepidopterología. 7 indexed citations

13.

Foo, Jasmine, Lin Liu, Kevin Leder, et al.. (2015). An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer. PLoS Computational Biology. 11(9). e1004350–e1004350. 24 indexed citations

14.

Foo, Jasmine, Kevin Leder, & Junfeng Zhu. (2014). Escape times for branching processes with random mutational fitness effects. Stochastic Processes and their Applications. 124(11). 3661–3697. 10 indexed citations

15.

Foo, Jasmine & Franziska Michor. (2014). Evolution of acquired resistance to anti-cancer therapy. Journal of Theoretical Biology. 355. 10–20. 188 indexed citations

16.

Foo, Jasmine, Kevin Leder, & Marc D. Ryser. (2014). Multifocality and recurrence risk: A quantitative model of field cancerization. Journal of Theoretical Biology. 355. 170–184. 22 indexed citations

17.

Foo, Jasmine & Kevin Leder. (2012). Rare events in cancer recurrence timing. Winter Simulation Conference. 1–10. 1 indexed citations

18.

Shu, Jingmin, Zhilian Xia, Lihua Li, et al.. (2012). Dose-dependent differential mRNA target selection and regulation by let-7a-7f and miR-17-92 cluster microRNAs. RNA Biology. 9(10). 1275–1287. 66 indexed citations

19.

Tang, Min, et al.. (2012). Selection pressure exerted by imatinib therapy leads to disparate outcomes of imatinib discontinuation trials. Haematologica. 97(10). 1553–1561. 31 indexed citations

20.

Foo, Jasmine, Juliann Chmielecki, William Pao, & Franziska Michor. (2012). Effects of Pharmacokinetic Processes and Varied Dosing Schedules on the Dynamics of Acquired Resistance to Erlotinib in EGFR-Mutant Lung Cancer. Journal of Thoracic Oncology. 7(10). 1583–1593. 66 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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