Dario Kringel

581 total citations
28 papers, 412 citations indexed

About

Dario Kringel is a scholar working on Physiology, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, Dario Kringel has authored 28 papers receiving a total of 412 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Physiology, 6 papers in Molecular Biology and 6 papers in Cognitive Neuroscience. Recurrent topics in Dario Kringel's work include Pain Mechanisms and Treatments (9 papers), Pain Management and Placebo Effect (6 papers) and Pain Management and Opioid Use (5 papers). Dario Kringel is often cited by papers focused on Pain Mechanisms and Treatments (9 papers), Pain Management and Placebo Effect (6 papers) and Pain Management and Opioid Use (5 papers). Dario Kringel collaborates with scholars based in Germany, Finland and Canada. Dario Kringel's co-authors include Jörn Lötsch, Alfred Ultsch, Eija Kalso, Thomas Hummel, Mari Kaunisto, Tuomo J Meretoja, Reetta Sipilä, Tiina Tasmuth, Ann‐Mari Estlander and Jeffrey S. Mogil and has published in prestigious journals such as PLoS ONE, Scientific Reports and Pain.

In The Last Decade

Dario Kringel

25 papers receiving 406 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dario Kringel Germany 12 105 71 62 60 57 28 412
Tomiko Oskotsky United States 14 44 0.4× 151 2.1× 87 1.4× 23 0.4× 6 0.1× 27 539
Charles S. Venuto United States 16 50 0.5× 145 2.0× 55 0.9× 10 0.2× 5 0.1× 40 759
Brian McNamara Ireland 13 32 0.3× 113 1.6× 41 0.7× 41 0.7× 8 0.1× 42 482
Michael P. McDermott United States 17 78 0.7× 172 2.4× 149 2.4× 13 0.2× 14 0.2× 41 816
Maria Ursino Italy 11 66 0.6× 106 1.5× 34 0.5× 7 0.1× 5 0.1× 15 492
Georg Ferber United Kingdom 17 74 0.7× 444 6.3× 71 1.1× 7 0.1× 10 0.2× 62 1.1k
Nicholas C. Firth United Kingdom 10 37 0.4× 117 1.6× 61 1.0× 32 0.5× 6 0.1× 15 429
Dev Mehta United States 7 255 2.4× 170 2.4× 56 0.9× 7 0.1× 24 0.4× 19 573
Won-Mo Jung South Korea 13 38 0.4× 34 0.5× 83 1.3× 111 1.9× 2 0.0× 38 624

Countries citing papers authored by Dario Kringel

Since Specialization
Citations

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

Fields of papers citing papers by Dario Kringel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dario Kringel

This figure shows the co-authorship network connecting the top 25 collaborators of Dario Kringel. A scholar is included among the top collaborators of Dario Kringel 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 Dario Kringel. Dario Kringel 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.
Lötsch, Jörn, et al.. (2026). Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data. European Journal of Pain. 30(2). e70221–e70221.
2.
Lötsch, Jörn, et al.. (2025). Dimensionality-modulated generative AI for safe biomedical dataset augmentation. iScience. 29(1). 114321–114321.
3.
Kringel, Dario & Jörn Lötsch. (2025). Knowledge of the genetics of human pain gained over the last decade from next-generation sequencing. Pharmacological Research. 214. 107667–107667.
4.
Lötsch, Jörn, Dario Kringel, & Alfred Ultsch. (2024). Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods. Biomedicines. 12(8). 1639–1639. 3 indexed citations
5.
Lötsch, Jörn, et al.. (2023). Machine learning analysis predicts a person’s sex based on mechanical but not thermal pain thresholds. Scientific Reports. 13(1). 2 indexed citations
6.
Kringel, Dario, et al.. (2022). Pharmacological data science perspective on fatal incidents of morphine treatment. Pharmacology & Therapeutics. 241. 108312–108312. 5 indexed citations
7.
Kringel, Dario, Sebastian Malkusch, Eija Kalso, & Jörn Lötsch. (2021). Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain. International Journal of Molecular Sciences. 22(2). 878–878. 2 indexed citations
8.
Kringel, Dario, Sebastian Malkusch, & Jörn Lötsch. (2021). Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis. International Journal of Molecular Sciences. 22(14). 7250–7250. 12 indexed citations
9.
Lötsch, Jörn, Dario Kringel, Gerd Geißlinger, et al.. (2020). Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes. International Journal of Molecular Sciences. 21(12). 4367–4367. 4 indexed citations
10.
Kringel, Dario, Mari Kaunisto, Eija Kalso, & Jörn Lötsch. (2019). Machine-learned analysis of global and glial/opioid intersection–related DNA methylation in patients with persistent pain after breast cancer surgery. Clinical Epigenetics. 11(1). 167–167. 15 indexed citations
11.
Kringel, Dario, Mari Kaunisto, Eija Kalso, & Jörn Lötsch. (2019). Machine-learned analysis of the association of next-generation sequencing–based genotypes with persistent pain after breast cancer surgery. Pain. 160(10). 2263–2277. 11 indexed citations
12.
Lötsch, Jörn, Reetta Sipilä, Tiina Tasmuth, et al.. (2018). Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy. Breast Cancer Research and Treatment. 171(2). 399–411. 53 indexed citations
13.
Kringel, Dario, et al.. (2018). Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain. Frontiers in Pharmacology. 9. 1008–1008. 4 indexed citations
14.
Kringel, Dario, et al.. (2018). A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes. European Journal of Pain. 22(10). 1735–1756. 10 indexed citations
15.
16.
Kringel, Dario, et al.. (2018). Computational Functional Genomics-Based Approaches in Analgesic Drug Discovery and Repurposing. Pharmacogenomics. 19(9). 783–797. 18 indexed citations
17.
Lötsch, Jörn, et al.. (2017). Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective. Frontiers in Molecular Neuroscience. 10. 252–252. 12 indexed citations
18.
Kringel, Dario, Marco Sisignano, Sebastian Zinn, & Jörn Lötsch. (2017). Next-generation sequencing of the human TRPV1 gene and the regulating co-players LTB4R and LTB4R2 based on a custom AmpliSeq™ panel. PLoS ONE. 12(6). e0180116–e0180116. 7 indexed citations
19.
Kringel, Dario & Jörn Lötsch. (2016). Next-generation sequencing of human opioid receptor genes based on a custom AmpliSeq™ library and ion torrent personal genome machine. Clinica Chimica Acta. 463. 32–38. 6 indexed citations
20.
Kringel, Dario, Alfred Ultsch, Michael Zimmermann, et al.. (2016). Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses. The Pharmacogenomics Journal. 17(5). 419–426. 23 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

Breakdown of academic impact, for papers by Tomiko Oskotsky Breakdown of academic impact, for papers by Charles S. Venuto Breakdown of academic impact, for papers by Brian McNamara Breakdown of academic impact, for papers by Michael P. McDermott Breakdown of academic impact, for papers by Maria Ursino Breakdown of academic impact, for papers by Georg Ferber Breakdown of academic impact, for papers by Nicholas C. Firth Breakdown of academic impact, for papers by Dev Mehta Breakdown of academic impact, for papers by Won-Mo Jung
Rankless by CCL
2026