Mary E. Spilker

4.5k total citations · 2 hit papers
44 papers, 3.2k citations indexed

About

Mary E. Spilker is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Mary E. Spilker has authored 44 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 12 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Oncology. Recurrent topics in Mary E. Spilker's work include Metabolomics and Mass Spectrometry Studies (5 papers), Medical Imaging Techniques and Applications (4 papers) and MRI in cancer diagnosis (4 papers). Mary E. Spilker is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (5 papers), Medical Imaging Techniques and Applications (4 papers) and MRI in cancer diagnosis (4 papers). Mary E. Spilker collaborates with scholars based in United States, Germany and United Kingdom. Mary E. Spilker's co-authors include Gary Siuzdak, Carlos Guijas, J. Rafael Montenegro-Burke, Benedikt Warth, Peter L. Zock, Ronald P. Mensink, Mira Katan, P.L.L. Goyens, Paolo Vicini and Till Sprenger and has published in prestigious journals such as Circulation, Nature Biotechnology and Bioinformatics.

In The Last Decade

Mary E. Spilker

44 papers receiving 3.1k 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.

METLIN: A Technology Platform for Identifying Knowns and Unknowns

2018 687 citations Carlos Guijas, J. Rafael Montenegro-Burke et al. profile →
Metabolomics activity screening for identifying metabolites that modulate phenotype

2018 349 citations Carlos Guijas, J. Rafael Montenegro-Burke et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mary E. Spilker United States	21	1.2k	476	421	345	316	44	3.2k
Weiguo Zhang China	34	824 0.7×	977 2.1×	493 1.2×	287 0.8×	242 0.8×	157	4.0k
Kyoungmi Kim United States	29	1.7k 1.4×	190 0.4×	715 1.7×	266 0.8×	152 0.5×	103	3.8k
Tao Bo China	37	1.7k 1.4×	300 0.6×	398 0.9×	115 0.3×	89 0.3×	194	4.0k
Hélène Blasco France	41	1.7k 1.4×	195 0.4×	582 1.4×	153 0.4×	160 0.5×	197	5.0k
Amrita K. Cheema United States	35	3.1k 2.5×	744 1.6×	991 2.4×	207 0.6×	422 1.3×	144	5.0k
Angela Maria Amorini Italy	37	1.4k 1.1×	315 0.7×	498 1.2×	152 0.4×	165 0.5×	100	3.9k
Sin Man Lam China	36	2.4k 1.9×	154 0.3×	880 2.1×	231 0.7×	232 0.7×	137	4.6k
Akiko Matsumoto Japan	32	1.2k 1.0×	364 0.8×	405 1.0×	76 0.2×	171 0.5×	165	3.9k
Yan Gao China	41	2.8k 2.2×	175 0.4×	563 1.3×	148 0.4×	335 1.1×	234	6.0k
Joanne E. Curran United States	40	2.3k 1.8×	343 0.7×	809 1.9×	440 1.3×	131 0.4×	197	5.8k

Countries citing papers authored by Mary E. Spilker

Since Specialization

Citations

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

Fields of papers citing papers by Mary E. Spilker

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary E. Spilker. 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 Mary E. Spilker. The network helps show where Mary E. Spilker may publish in the future.

Co-authorship network of co-authors of Mary E. Spilker

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

All Works

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20 of 20 papers shown

Beattie, Kylie A., Meghna Verma, Richard Brennan, et al.. (2024). Quantitative systems toxicology modeling in pharmaceutical research and development: An industry‐wide survey and selected case study examples. CPT Pharmacometrics & Systems Pharmacology. 13(12). 2036–2051. 6 indexed citations

Rakhilin, Nikolai, Bing Yang, Mary E. Spilker, et al.. (2023). Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue. Journal of Controlled Release. 354. 244–259. 3 indexed citations

Boras, Britton, et al.. (2020). Predicting Chemotherapy-Induced Neutropenia and Granulocyte Colony–Stimulating Factor Response Using Model-Based In Vitro to Clinical Translation. The AAPS Journal. 22(6). 143–143. 6 indexed citations

Kindt, Erick, et al.. (2020). A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships. The AAPS Journal. 22(2). 53–53. 22 indexed citations

Boras, Britton, Tae Sung, Yanke Yu, et al.. (2020). A physiological model of granulopoiesis to predict clinical drug induced neutropenia from in vitro bone marrow studies: with application to a cell cycle inhibitor. Journal of Pharmacokinetics and Pharmacodynamics. 47(2). 163–182. 11 indexed citations

Guijas, Carlos, J. Rafael Montenegro-Burke, Benedikt Warth, Mary E. Spilker, & Gary Siuzdak. (2018). Metabolomics activity screening for identifying metabolites that modulate phenotype. Nature Biotechnology. 36(4). 316–320. 349 indexed citations breakdown →

Giddabasappa, Anand, Vijay Gupta, Parul Gupta, et al.. (2016). Biodistribution and Targeting of Anti-5T4 Antibody–Drug Conjugate Using Fluorescence Molecular Tomography. Molecular Cancer Therapeutics. 15(10). 2530–2540. 26 indexed citations

Spilker, Mary E., Xiaoying Chen, Ravi Visswanathan, et al.. (2016). Found in Translation: Maximizing the Clinical Relevance of Nonclinical Oncology Studies. Clinical Cancer Research. 23(4). 1080–1090. 23 indexed citations

Palsson, Sirus, Timothy P. Hickling, Erica L. Bradshaw‐Pierce, et al.. (2013). The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models. BMC Systems Biology. 7(1). 95–95. 63 indexed citations

10.

Zhang, Cathy C., Zhengming Yan, Wenlin Li, et al.. (2011). [18F]FLT–PET Imaging Does Not Always “Light Up” Proliferating Tumor Cells. Clinical Cancer Research. 18(5). 1303–1312. 62 indexed citations

11.

Spilker, Mary E., Justine L. Lam, Bernadette Pascual, et al.. (2011). Effective Targeting of Hedgehog Signaling in a Medulloblastoma Model with PF-5274857, a Potent and Selective Smoothened Antagonist That Penetrates the Blood–Brain Barrier. Molecular Cancer Therapeutics. 11(1). 57–65. 44 indexed citations

12.

Boecker, Henning, Till Sprenger, Mary E. Spilker, et al.. (2008). The Runner's High: Opioidergic Mechanisms in the Human Brain. Cerebral Cortex. 18(11). 2523–2531. 332 indexed citations

13.

Miederer, Isabelle, Sibylle Ziegler, Mary E. Spilker, et al.. (2008). Kinetic modelling of [11C]flumazenil using data-driven methods. European Journal of Nuclear Medicine and Molecular Imaging. 36(4). 659–670. 14 indexed citations

14.

Zierhut, Matthew L., Jill C. Gardner, Mary E. Spilker, John T. Sharp, & Paolo Vicini. (2007). Kinetic Modeling of Contrast-Enhanced MRI: An Automated Technique for Assessing Inflammation in the Rheumatoid Arthritis Wrist. Annals of Biomedical Engineering. 35(5). 781–795. 14 indexed citations

15.

Goyens, P.L.L., Mary E. Spilker, Peter L. Zock, Mira Katan, & Ronald P. Mensink. (2006). Conversion of α-linolenic acid in humans is influenced by the absolute amounts of α-linolenic acid and linoleic acid in the diet and not by their ratio. American Journal of Clinical Nutrition. 84(1). 44–53. 285 indexed citations

16.

Goyens, P.L.L., Mary E. Spilker, Peter L. Zock, Mira Katan, & Ronald P. Mensink. (2005). Compartmental modeling to quantify α-linolenic acid conversion after longer term intake of multiple tracer boluses. Journal of Lipid Research. 46(7). 1474–1483. 120 indexed citations

17.

Spilker, Mary E., Kok‐Yong Seng, Heike E. Daldrup‐Link, et al.. (2005). Mixture model approach to tumor classification based on pharmacokinetic measures of tumor permeability. Journal of Magnetic Resonance Imaging. 22(4). 549–558. 5 indexed citations

18.

Spilker, Mary E., Till Sprenger, Michael Valet, et al.. (2004). Quantification of [18F]diprenorphine kinetics in the human brain with compartmental and non-compartmental modeling approaches. NeuroImage. 22(4). 1523–1533. 18 indexed citations

19.

Kerwin, William, Andrew C. Hooker, Mary E. Spilker, et al.. (2003). Quantitative Magnetic Resonance Imaging Analysis of Neovasculature Volume in Carotid Atherosclerotic Plaque. Circulation. 107(6). 851–856. 265 indexed citations

20.

Spilker, Mary E. & Paolo Vicini. (2001). An Evaluation of Extended vs Weighted Least Squares for Parameter Estimation in Physiological Modeling. Journal of Biomedical Informatics. 34(5). 348–364. 11 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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