Dean Bottino

979 total citations
31 papers, 625 citations indexed

About

Dean Bottino is a scholar working on Molecular Biology, Oncology and Hematology. According to data from OpenAlex, Dean Bottino has authored 31 papers receiving a total of 625 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 8 papers in Oncology and 7 papers in Hematology. Recurrent topics in Dean Bottino's work include Cancer Genomics and Diagnostics (6 papers), Chronic Lymphocytic Leukemia Research (5 papers) and Chronic Myeloid Leukemia Treatments (4 papers). Dean Bottino is often cited by papers focused on Cancer Genomics and Diagnostics (6 papers), Chronic Lymphocytic Leukemia Research (5 papers) and Chronic Myeloid Leukemia Treatments (4 papers). Dean Bottino collaborates with scholars based in United States, Japan and Switzerland. Dean Bottino's co-authors include George Oster, Alexander Mogilner, Murray Stewart, Lisa Fauci, Jackson Burton, Timothy W. Secomb, René Bruno, Jin Y. Jin, Robert C. Penland and Yanan Zheng and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Dean Bottino

29 papers receiving 596 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dean Bottino United States 13 188 144 130 86 83 31 625
Matthew Onsum United States 10 301 1.6× 81 0.6× 313 2.4× 89 1.0× 44 0.5× 24 633
Rui D. M. Travasso Portugal 18 400 2.1× 180 1.3× 79 0.6× 26 0.3× 153 1.8× 50 869
Gen Yang China 17 266 1.4× 66 0.5× 205 1.6× 189 2.2× 140 1.7× 65 736
Claudia Kalla Germany 12 308 1.6× 71 0.5× 74 0.6× 82 1.0× 67 0.8× 17 573
Guoping Ai China 17 185 1.0× 58 0.4× 46 0.4× 40 0.5× 30 0.4× 48 731
Arnaud Chauvière United States 13 151 0.8× 173 1.2× 133 1.0× 19 0.2× 66 0.8× 20 450
Sabine Dormann Germany 9 153 0.8× 112 0.8× 77 0.6× 22 0.3× 55 0.7× 13 431
Mohammad Jafarnejad United States 14 190 1.0× 27 0.2× 313 2.4× 47 0.5× 64 0.8× 17 595
Kevin G. Phillips United States 13 94 0.5× 55 0.4× 144 1.1× 70 0.8× 175 2.1× 36 750
Tamir Epstein United States 12 337 1.8× 46 0.3× 78 0.6× 36 0.4× 107 1.3× 21 704

Countries citing papers authored by Dean Bottino

Since Specialization
Citations

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

Fields of papers citing papers by Dean Bottino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dean Bottino

This figure shows the co-authorship network connecting the top 25 collaborators of Dean Bottino. A scholar is included among the top collaborators of Dean Bottino 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 Dean Bottino. Dean Bottino 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.
Otani, Yuki, Richard Labotka, Mark Rogge, et al.. (2024). Modeling serum M‐protein response for early detection of biochemical relapse in myeloma patients treated with bortezomib, lenalidomide and dexamethasone. CPT Pharmacometrics & Systems Pharmacology. 13(12). 2124–2136.
2.
Yuan, Lin, Dean Bottino, Greg Hather, et al.. (2023). Heterogeneity in Vaccinal Immunity to SARS-CoV-2 Can Be Addressed by a Personalized Booster Strategy. Vaccines. 11(4). 806–806. 2 indexed citations
3.
Ruiz-Garcı́a, Ana, Paul Baverel, Dean Bottino, et al.. (2023). A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development. Journal of Pharmacokinetics and Pharmacodynamics. 50(3). 147–172. 28 indexed citations
4.
Lemaire, Vincent, et al.. (2022). From Cold to Hot: Changing Perceptions and Future Opportunities for Quantitative Systems Pharmacology Modeling in Cancer Immunotherapy. Clinical Pharmacology & Therapeutics. 113(5). 963–972. 15 indexed citations
5.
Kondic, Anna, Dean Bottino, John M. Harrold, et al.. (2022). Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making. Frontiers in Pharmacology. 13. 6 indexed citations
6.
Bottino, Dean, et al.. (2021). Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection. PubMed. 4(3). 144–148. 3 indexed citations
7.
Bottino, Dean, Rachael Liu, Hojjat Bazzazi, & Karthik Venkatakrishnan. (2020). Quantitative Translation in Immuno‐Oncology Research and Development. Clinical Pharmacology & Therapeutics. 108(3). 430–433. 1 indexed citations
8.
Bottino, Dean, Jilai Zhou, Chirag Patel, et al.. (2019). Dose Optimization for Anticancer Drug Combinations: Maximizing Therapeutic Index via Clinical Exposure-Toxicity/Preclinical Exposure-Efficacy Modeling. Clinical Cancer Research. 25(22). 6633–6643. 17 indexed citations
9.
Bruno, René, Dean Bottino, Dinesh P. de Alwis, et al.. (2019). Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models. Clinical Cancer Research. 26(8). 1787–1795. 61 indexed citations
10.
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12.
Johnson, Kaitlyn E., Jackson Burton, Dougľas R. White, et al.. (2019). Directional inconsistency between Response Evaluation Criteria in Solid Tumors (RECIST) time to progression and response speed and depth. European Journal of Cancer. 109. 196–203. 7 indexed citations
13.
Burton, Jackson, Dean Bottino, & Timothy W. Secomb. (2019). A Systems Pharmacology Model for Drug Delivery to Solid Tumors by Antibody-Drug Conjugates: Implications for Bystander Effects. The AAPS Journal. 22(1). 12–12. 27 indexed citations
14.
Neal, Joel W., Robert C. Doebele, Gregory J. Riely, et al.. (2018). P1.13-44 Safety, PK, and Preliminary Antitumor Activity of the Oral EGFR/HER2 Exon 20 Inhibitor TAK-788 in NSCLC. Journal of Thoracic Oncology. 13(10). S599–S599. 24 indexed citations
15.
Burton, Jackson, et al.. (2015). Abstract 1636: A model-based approach toward clinical pipeline optimization. Cancer Research. 75(15_Supplement). 1636–1636. 1 indexed citations
16.
Lassen, Ulrik, Didier Meulendijks, L.L. Siu, et al.. (2014). A Phase I Monotherapy Study of RG7212, a First-in-Class Monoclonal Antibody Targeting TWEAK Signaling in Patients with Advanced Cancers. Clinical Cancer Research. 21(2). 258–266. 32 indexed citations
17.
Stein, Andrew M., Dean Bottino, Vijay Modur, et al.. (2011). BCR–ABL Transcript Dynamics Support the Hypothesis That Leukemic Stem Cells Are Reduced during Imatinib Treatment. Clinical Cancer Research. 17(21). 6812–6821. 32 indexed citations
18.
Bottino, Dean, Andrew M. Stein, Ani Georgieva, et al.. (2009). Inference of imatinib (IM) effects on leukemic stem cell (SC) compartment via mathematical modeling of IRIS treatment response data. Journal of Clinical Oncology. 27(15_suppl). 7056–7056. 2 indexed citations
19.
Bottino, Dean & Lisa Fauci. (1998). A computational model of ameboid deformation and locomotion. European Biophysics Journal. 27(5). 532–539. 54 indexed citations
20.
Bottino, Dean. (1998). Modeling Viscoelastic Networks and Cell Deformation in the Context of the Immersed Boundary Method. Journal of Computational Physics. 147(1). 86–113. 50 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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