Brian E. Carlson

1.7k total citations
56 papers, 904 citations indexed

About

Brian E. Carlson is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Physiology. According to data from OpenAlex, Brian E. Carlson has authored 56 papers receiving a total of 904 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cardiology and Cardiovascular Medicine, 11 papers in Molecular Biology and 9 papers in Physiology. Recurrent topics in Brian E. Carlson's work include Cardiovascular Function and Risk Factors (10 papers), Cardiac electrophysiology and arrhythmias (7 papers) and Cardiovascular Health and Disease Prevention (7 papers). Brian E. Carlson is often cited by papers focused on Cardiovascular Function and Risk Factors (10 papers), Cardiac electrophysiology and arrhythmias (7 papers) and Cardiovascular Health and Disease Prevention (7 papers). Brian E. Carlson collaborates with scholars based in United States, New Zealand and United Kingdom. Brian E. Carlson's co-authors include Timothy W. Secomb, Julia Arciero, Daniel Beard, Richard A. Erickson, Maxwell L. Neal, John H. Gennari, Daniel L. Cook, Mette S. Olufsen, Andrew S. Greene and Ranjan K. Dash and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Gastroenterology.

In The Last Decade

Brian E. Carlson

51 papers receiving 882 citations

Peers

Brian E. Carlson

CCM

PRM

SURGE

PHYSI

Leroy L. Cooper United States

Qiao Li China

Xiaofei Gao China

Xiang Ma China

Midoriko Higashi Japan

Jianbing Zhu China

Matthias Lutz Germany

Jun Jiang China

Hidekazu Kondo Japan

Pascal Challande France

Leroy L. Cooper United States

Brian E. Carlson

546 ×1.9

CCM

292 ×1.4

136 ×0.7

PRM

154 ×0.9

SURGE

97 ×0.7

PHYSI

Citations per year, relative to Brian E. Carlson Brian E. Carlson (= 1×) peers Leroy L. Cooper

Countries citing papers authored by Brian E. Carlson

Since Specialization

Citations

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

Fields of papers citing papers by Brian E. Carlson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian E. Carlson

This figure shows the co-authorship network connecting the top 25 collaborators of Brian E. Carlson. A scholar is included among the top collaborators of Brian E. Carlson 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 Brian E. Carlson. Brian E. Carlson 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

Gu, Feng, Andreas Meyer, Tonimarie Catalan, et al.. (2025). Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure. npj Digital Medicine. 8(1). 110–110. 5 indexed citations

Liu, Yijun, Zhen Zhao, Qi Yu, et al.. (2025). Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction. EClinicalMedicine. 88. 103462–103462. 1 indexed citations

Carlson, Brian E., et al.. (2025). Artificial intelligence for manuscript writing: policies and implementation in cardiovascular journals. Cardiovascular Diagnosis and Therapy. 15(5). 1107–1112.

Hummel, Scott L., et al.. (2021). Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning. The Journal of Physiology. 599(22). 4991–5013. 25 indexed citations

Carlson, Brian E., et al.. (2021). Systems analysis of the mechanisms governing the cardiovascular response to changes in posture and in peripheral demand during exercise. Journal of Molecular and Cellular Cardiology. 163. 33–55. 13 indexed citations

Gu, Feng, Steven E. Whitesall, Kimber Converso‐Baran, et al.. (2020). Potential role of intermittent functioning of baroreflexes in the etiology of hypertension in spontaneously hypertensive rats. JCI Insight. 5(19). 11 indexed citations

Kalbasi, Reza, John H. Gennari, Brian E. Carlson, et al.. (2019). Model annotation and discovery with the Physiome Model Repository. BMC Bioinformatics. 20(1). 457–457. 12 indexed citations

Carlson, Brian E., et al.. (2018). Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model. Mathematical Biosciences. 304. 9–24. 35 indexed citations

Lee, Pilhwa, Brian E. Carlson, Naomi C. Chesler, et al.. (2016). Heterogeneous mechanics of the mouse pulmonary arterial network. Biomechanics and Modeling in Mechanobiology. 15(5). 1245–1261. 10 indexed citations

10.

Torres, Carlos, et al.. (2015). Enhanced Effects of Isoflurane on the Long QT Syndrome 1–associated A341V Mutant. Anesthesiology. 122(4). 806–820. 7 indexed citations

11.

Carlson, Brian E., Jim O. Vigoreaux, & David W. Maughan. (2014). Diffusion Coefficients of Endogenous Cytosolic Proteins from Rabbit Skinned Muscle Fibers. Biophysical Journal. 106(4). 780–792. 7 indexed citations

12.

Neal, Maxwell L., Michael T. Cooling, Lucian P. Smith, et al.. (2014). A Reappraisal of How to Build Modular, Reusable Models of Biological Systems. PLoS Computational Biology. 10(10). e1003849–e1003849. 31 indexed citations

13.

Menzies, Robert, Robert J. Unwin, Ranjan K. Dash, et al.. (2013). Effect of P2X4 and P2X7 receptor antagonism on the pressure diuresis relationship in rats. Frontiers in Physiology. 4. 305–305. 37 indexed citations

14.

Carlson, Brian E., et al.. (2013). Innovative Use of Ontologies: Association of Physiological Pathways to Corresponding Computational Models and Disparate Data Types.. AMIA. 1 indexed citations

15.

Tewari, Shivendra G., Scott M. Bugenhagen, Zhijie Wang, et al.. (2013). Analysis of cardiovascular dynamics in pulmonary hypertensive C57BL6/J mice. Frontiers in Physiology. 4. 355–355. 20 indexed citations

16.

Bassingthwaighte, James B., Daniel Beard, Brian E. Carlson, Ranjan K. Dash, & Kalyan C. Vinnakota. (2012). Modeling to Link Regional Myocardial Work, Metabolism and Blood Flows. Annals of Biomedical Engineering. 40(11). 2379–2398. 13 indexed citations

17.

Beard, Daniel, et al.. (2012). Multiscale Modeling and Data Integration in the Virtual Physiological Rat Project. Annals of Biomedical Engineering. 40(11). 2365–2378. 32 indexed citations

18.

Arciero, Julia, Brian E. Carlson, & Timothy W. Secomb. (2008). Theoretical model of metabolic blood flow regulation: roles of ATP release by red blood cells and conducted responses. American Journal of Physiology-Heart and Circulatory Physiology. 295(4). H1562–H1571. 83 indexed citations

19.

Carlson, Brian E. & Timothy W. Secomb. (2004). A theoretical model for the myogenic response based on the mechanics of vascular smooth muscle. The FASEB Journal. 18. 4326. 2 indexed citations

20.

Carlson, Brian E., et al.. (2004). Blood donor white blood cell reduction filters as a source of human peripheral blood–derived endothelial progenitor cells. Transfusion. 45(1). 21–25. 22 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.

Explore authors with similar magnitude of impact