Lisa Tucker‐Kellogg

1.9k total citations
59 papers, 1.3k citations indexed

About

Lisa Tucker‐Kellogg is a scholar working on Molecular Biology, Cell Biology and Hepatology. According to data from OpenAlex, Lisa Tucker‐Kellogg has authored 59 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 9 papers in Cell Biology and 9 papers in Hepatology. Recurrent topics in Lisa Tucker‐Kellogg's work include Gene Regulatory Network Analysis (9 papers), Liver physiology and pathology (8 papers) and Computational Drug Discovery Methods (7 papers). Lisa Tucker‐Kellogg is often cited by papers focused on Gene Regulatory Network Analysis (9 papers), Liver physiology and pathology (8 papers) and Computational Drug Discovery Methods (7 papers). Lisa Tucker‐Kellogg collaborates with scholars based in Singapore, United States and China. Lisa Tucker‐Kellogg's co-authors include Jonathan King, Peter W. Shor, Bonnie Berger, Bruce Tidor, Lakshmi Venkatraman, Robert G. Griffin, Chad M. Rienstra, M. Hohwy, Michael T. McMahon and Bernd Reif and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Lisa Tucker‐Kellogg

59 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lisa Tucker‐Kellogg Singapore 19 539 204 151 139 108 59 1.3k
Henryk Mach United States 19 1.0k 1.9× 74 0.4× 124 0.8× 158 1.1× 56 0.5× 34 1.4k
Thomas W. Patapoff United States 31 2.0k 3.7× 149 0.7× 170 1.1× 50 0.4× 31 0.3× 65 2.9k
Mari Shimura Japan 22 1.0k 1.9× 30 0.1× 44 0.3× 150 1.1× 27 0.3× 70 1.8k
John R. Kettman United States 26 1.3k 2.5× 147 0.7× 116 0.8× 65 0.5× 61 0.6× 77 3.1k
L J Kienker United States 9 882 1.6× 54 0.3× 87 0.6× 41 0.3× 39 0.4× 10 1.4k
Marcel Kwiatkowski Germany 16 412 0.8× 154 0.8× 15 0.1× 53 0.4× 36 0.3× 59 884
Yogesh K. Gupta United States 21 1.0k 1.9× 95 0.5× 91 0.6× 75 0.5× 87 0.8× 37 1.6k
Bruce Neri United States 23 1.6k 3.1× 101 0.5× 32 0.2× 27 0.2× 87 0.8× 37 2.4k
Tadakazu Maeda Japan 27 1.1k 2.0× 291 1.4× 158 1.0× 236 1.7× 48 0.4× 72 1.9k
André Ziegler Switzerland 18 1.2k 2.3× 47 0.2× 59 0.4× 123 0.9× 47 0.4× 35 1.8k

Countries citing papers authored by Lisa Tucker‐Kellogg

Since Specialization
Citations

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

Fields of papers citing papers by Lisa Tucker‐Kellogg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lisa Tucker‐Kellogg. 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 Lisa Tucker‐Kellogg. The network helps show where Lisa Tucker‐Kellogg may publish in the future.

Co-authorship network of co-authors of Lisa Tucker‐Kellogg

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

All Works

20 of 20 papers shown
1.
Lee, Younghwan, Radoslaw M. Sobota, Aaron T. Irving, et al.. (2024). Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism. eLife. 13. 2 indexed citations
2.
Wu, Xiaolin, Francesca Lim, Yie Hou Lee, et al.. (2024). A high-density microfluidic bioreactor for the automated manufacturing of CAR T cells. Nature Biomedical Engineering. 8(12). 1571–1591. 22 indexed citations
3.
Aloweni, Fazila, Siew Hoon Lim, Shin Yuh Ang, et al.. (2023). Evaluation of an Evidence‐Based Care Bundle for Preventing Hospital‐Acquired Pressure Injuries in High‐Risk Surgical Patients. AORN Journal. 118(5). 306–320. 6 indexed citations
4.
Hamadee, Nur Hidayah, et al.. (2023). Myoglobin-derived iron causes wound enlargement and impaired regeneration in pressure injuries of muscle. eLife. 12. 6 indexed citations
5.
Corrias, Alberto, et al.. (2022). The panniculus carnosus muscle: a missing link in the chronicity of heel pressure ulcers?. Journal of The Royal Society Interface. 19(187). 20210631–20210631. 6 indexed citations
6.
7.
Rouers, Angéline, Melissa Hui Yen Chng, Bernett Lee, et al.. (2021). Immune cell phenotypes associated with disease severity and long-term neutralizing antibody titers after natural dengue virus infection. Cell Reports Medicine. 2(5). 100278–100278. 23 indexed citations
8.
Welsch, Roy E., et al.. (2020). Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions. Bioinformatics. 36(9). 2813–2820. 6 indexed citations
9.
Hirpara, Jayshree L., Grégory Bellot, Jianhua Qu, et al.. (2019). Superoxide induced inhibition of death receptor signaling is mediated via induced expression of apoptosis inhibitory protein cFLIP. Redox Biology. 30. 101403–101403. 14 indexed citations
10.
Huang, Lu, et al.. (2018). Combination Therapy and the Evolution of Resistance: The Theoretical Merits of Synergism and Antagonism in Cancer. Cancer Research. 78(9). 2419–2431. 59 indexed citations
11.
Lei, Zhengdeng, Simran Kaur, Nathan Harmston, et al.. (2017). Wnt proteins synergize to activate β-catenin signaling. Journal of Cell Science. 130(9). 1532–1544. 57 indexed citations
12.
13.
Nim, Hieu T., Jacob White, & Lisa Tucker‐Kellogg. (2013). SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments. Nucleic Acids Research. 41(W1). W187–W191. 3 indexed citations
14.
Chia, Ser‐Mien, et al.. (2012). HGF regulates the activation of TGF‐β1 in rat hepatocytes and hepatic stellate cells. Journal of Cellular Physiology. 228(2). 393–401. 34 indexed citations
15.
Venkatraman, Lakshmi, Ser‐Mien Chia, Jacob White, et al.. (2012). Plasmin Antagonizes Positive Feedback Between TGF-β1 and TSP1 : Steady States and Dynamics. Biophysical Journal. 102(3). 730a–731a. 1 indexed citations
16.
Hirpara, Jayshree L., et al.. (2012). FLIP: A flop for execution signals. Cancer Letters. 332(2). 151–155. 17 indexed citations
17.
Zheng, Baixue, Weimiao Yu, Yan Wang, et al.. (2011). Predicting In Vivo Anti-Hepatofibrotic Drug Efficacy Based on In Vitro High-Content Analysis. PLoS ONE. 6(11). e26230–e26230. 3 indexed citations
18.
Pan, Catherine, Baowen Li, Lisa Tucker‐Kellogg, et al.. (2011). Simulating EGFR-ERK Signaling Control by Scaffold Proteins KSR and MP1 Reveals Differential Ligand-Sensitivity Co-Regulated by Cbl-CIN85 and Endophilin. PLoS ONE. 6(8). e22933–e22933. 9 indexed citations
19.
Venkatraman, Lakshmi, et al.. (2009). THE STEADY STATES AND DYNAMICS OF UROKINASE-MEDIATED PLASMIN ACTIVATION. WORLD SCIENTIFIC eBooks. 190–200. 2 indexed citations
20.
Rienstra, Chad M., Lisa Tucker‐Kellogg, Christopher P. Jaroniec, et al.. (2002). De novo determination of peptide structure with solid-state magic-angle spinning NMR spectroscopy. Proceedings of the National Academy of Sciences. 99(16). 10260–10265. 226 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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