Natalia A. Trayanova

18.6k total citations · 5 hit papers
422 papers, 12.4k citations indexed

About

Natalia A. Trayanova is a scholar working on Cardiology and Cardiovascular Medicine, Cellular and Molecular Neuroscience and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Natalia A. Trayanova has authored 422 papers receiving a total of 12.4k indexed citations (citations by other indexed papers that have themselves been cited), including 345 papers in Cardiology and Cardiovascular Medicine, 80 papers in Cellular and Molecular Neuroscience and 73 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Natalia A. Trayanova's work include Cardiac electrophysiology and arrhythmias (268 papers), Cardiac Arrhythmias and Treatments (120 papers) and Neuroscience and Neural Engineering (78 papers). Natalia A. Trayanova is often cited by papers focused on Cardiac electrophysiology and arrhythmias (268 papers), Cardiac Arrhythmias and Treatments (120 papers) and Neuroscience and Neural Engineering (78 papers). Natalia A. Trayanova collaborates with scholars based in United States, United Kingdom and Canada. Natalia A. Trayanova's co-authors include Patrick M. Boyle, Hermenegild Arevalo, Gernot Plank, Jason D. Bayer, Felipe Aguel, Fijoy Vadakkumpadan, Viatcheslav Gurev, Jason Constantino, Edward J. Vigmond and Robert Blake and has published in prestigious journals such as The Lancet, Circulation and Journal of Clinical Investigation.

In The Last Decade

Natalia A. Trayanova

398 papers receiving 12.2k citations

Hit Papers

A Novel Rule-Based Algori... 2012 2026 2016 2021 2012 2016 2018 2024 2025 100 200 300
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.

  1. A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models
    2012 379 citations Robert Blake, Natalia A. Trayanova et al. profile →
  2. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models
    2016 293 citations Hermenegild Arevalo, Fijoy Vadakkumpadan et al. profile →
  3. Computational models in cardiology
    2018 273 citations Natalia A. Trayanova et al. profile →
  4. Digital twins in medicine
    2024 78 citations Reinhard Laubenbacher, Borna Mehrad et al. Nature Computational Science profile →
  5. State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology
    2025 14 citations Emma Svennberg, Janet K. Han et al. EP Europace profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Natalia A. Trayanova United States 60 9.8k 2.4k 2.1k 1.6k 1.6k 422 12.4k
Igor R. Efimov United States 58 7.0k 0.7× 3.5k 1.4× 2.3k 1.1× 1.6k 1.0× 797 0.5× 319 10.9k
Yoram Rudy United States 69 15.1k 1.5× 8.3k 3.4× 3.0k 1.4× 812 0.5× 1.5k 0.9× 237 17.5k
Peter Hunter New Zealand 56 4.8k 0.5× 2.8k 1.1× 667 0.3× 3.1k 2.0× 1.3k 0.8× 286 11.1k
Peng‐Sheng Chen United States 65 13.3k 1.4× 4.9k 2.0× 1.5k 0.7× 368 0.2× 626 0.4× 374 15.9k
Raimond L. Winslow United States 51 4.3k 0.4× 4.7k 1.9× 1.2k 0.6× 521 0.3× 1.2k 0.7× 163 8.4k
Patrick D. Wolf United States 42 3.3k 0.3× 618 0.3× 1.2k 0.6× 1.2k 0.8× 1.1k 0.7× 170 5.3k
José Jalife United States 87 18.9k 1.9× 8.1k 3.4× 3.3k 1.6× 1.1k 0.7× 974 0.6× 350 24.2k
Alexander V. Panfilov Belgium 51 6.7k 0.7× 3.2k 1.3× 1.4k 0.7× 904 0.6× 524 0.3× 219 9.7k
Gernot Plank Austria 44 5.2k 0.5× 789 0.3× 614 0.3× 1.2k 0.7× 954 0.6× 261 6.5k
Raymond E. Ideker United States 43 5.9k 0.6× 893 0.4× 864 0.4× 346 0.2× 1.1k 0.7× 267 6.7k

Countries citing papers authored by Natalia A. Trayanova

Since Specialization
Citations

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

Fields of papers citing papers by Natalia A. Trayanova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Natalia A. Trayanova. 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 Natalia A. Trayanova. The network helps show where Natalia A. Trayanova may publish in the future.

Co-authorship network of co-authors of Natalia A. Trayanova

This figure shows the co-authorship network connecting the top 25 collaborators of Natalia A. Trayanova. A scholar is included among the top collaborators of Natalia A. Trayanova 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 Natalia A. Trayanova. Natalia A. Trayanova 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.
2.
Mansfield, Catherine, et al.. (2025). Protective effect of ursodeoxycholic acid upon the post-myocardial infarction heart. Cardiovascular Research. 121(13). 1983–1985.
3.
Svennberg, Emma, Janet K. Han, Enrico G. Caiani, et al.. (2025). State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology. EP Europace. 27(5). 14 indexed citations breakdown →
4.
Piersanti, Roberto, et al.. (2025). Defining myocardial fiber bundle architecture in atrial digital twins. Computers in Biology and Medicine. 188. 109774–109774. 4 indexed citations
5.
Prakosa, Adityo, et al.. (2025). Heart Digital Twins Predict Features of Invasive Reentrant Circuits and Ablation Lesions in Scar-Dependent Ventricular Tachycardia. Circulation Arrhythmia and Electrophysiology. 18(8). e013660–e013660.
6.
Laubenbacher, Reinhard, Borna Mehrad, Ilya Shmulevich, & Natalia A. Trayanova. (2024). Digital twins in medicine. Nature Computational Science. 4(3). 184–191. 78 indexed citations breakdown →
7.
Sung, Eric, Usama A. Daimee, M. Engels, et al.. (2023). Evaluation of a deep learning‐enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias. The Journal of Physiology. 602(18). 4625–4644. 2 indexed citations
8.
Zhou, Shijie, et al.. (2021). Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 379(2212). 20200258–20200258. 32 indexed citations
9.
Prakosa, Adityo, Michael K. Southworth, Jennifer N. Avari Silva, Jonathan R. Silva, & Natalia A. Trayanova. (2021). Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation. Computers in Biology and Medicine. 133. 104366–104366. 13 indexed citations
10.
11.
Yu, Joseph Kwong‐Leung, William H. Franceschi, Farhad Pashakhanloo, et al.. (2021). Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models. Cardiovascular Research. 118(5). 1247–1261. 13 indexed citations
12.
Zhou, Shijie, Amir AbdelWahab, John L. Sapp, et al.. (2021). Assessment of an ECG‐Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease. Journal of the American Heart Association. 10(20). e022217–e022217. 8 indexed citations
13.
Shade, Julie K., Rheeda L. Ali, Dan M. Popescu, et al.. (2020). Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation. Circulation Arrhythmia and Electrophysiology. 13(7). e008213–e008213. 70 indexed citations
14.
Feeny, Albert, Mina K. Chung, Anant Madabhushi, et al.. (2020). Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circulation Arrhythmia and Electrophysiology. 13(8). e007952–e007952. 138 indexed citations
15.
Okada, David R., Jonathan Chrispin, Adityo Prakosa, et al.. (2020). Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy. Circulation Arrhythmia and Electrophysiology. 13(4). e007975–e007975. 33 indexed citations
16.
Izu, Leighton T., Peter Köhl, Penelope A. Boyden, et al.. (2019). Mechano‐electric and mechano‐chemo‐transduction in cardiomyocytes. The Journal of Physiology. 598(7). 1285–1305. 32 indexed citations
17.
Boyle, Patrick M., Joe B. Hakim, Sohail Zahid, et al.. (2018). The Fibrotic Substrate in Persistent Atrial Fibrillation Patients: Comparison Between Predictions From Computational Modeling and Measurements From Focal Impulse and Rotor Mapping. Frontiers in Physiology. 9. 1151–1151. 35 indexed citations
18.
19.
Ashikaga, Hiroshi, Hermenegild Arevalo, Fijoy Vadakkumpadan, et al.. (2011). Abstract 14174: MRI-Based Patient-Specific Virtual Electrophysiology Laboratory for Scar-Related Ventricular Tachycardia. Circulation. 124(suppl_21). 4 indexed citations
20.
Rodríguez, Blanca, et al.. (2007). Mechanistic investigation into the arrhythmogenic role of transmural heterogeneities in regional ischaemia phase 1A. EP Europace. 9(Supplement 6). vi46–vi58. 27 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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