Bernhard Steiert

1.7k total citations · 1 hit paper
15 papers, 1.0k citations indexed

About

Bernhard Steiert is a scholar working on Molecular Biology, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Bernhard Steiert has authored 15 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Artificial Intelligence and 4 papers in Statistics and Probability. Recurrent topics in Bernhard Steiert's work include Gene Regulatory Network Analysis (6 papers), Machine Learning in Healthcare (3 papers) and Computational Drug Discovery Methods (3 papers). Bernhard Steiert is often cited by papers focused on Gene Regulatory Network Analysis (6 papers), Machine Learning in Healthcare (3 papers) and Computational Drug Discovery Methods (3 papers). Bernhard Steiert collaborates with scholars based in Germany, Switzerland and United States. Bernhard Steiert's co-authors include Lucy Hutchinson, Solveig Badillo, Fabian Birzele, Jitao David Zhang, Iakov I. Davydov, Tony Kam‐Thong, Balázs Bánfai, Juliane Siebourg‐Polster, Clemens Kreutz and Jens Timmer and has published in prestigious journals such as Bioinformatics, PLoS ONE and Clinical Pharmacology & Therapeutics.

In The Last Decade

Bernhard Steiert

14 papers receiving 992 citations

Hit Papers

align trajectories

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.

An Introduction to Machine Learning

2020 503 citations Solveig Badillo, Balázs Bánfai et al. Clinical Pharmacology & Therapeutics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bernhard Steiert Germany	11	383	138	82	69	61	15	1.0k
Junyi Li China	21	465 1.2×	271 2.0×	115 1.4×	32 0.5×	65 1.1×	112	1.4k
Luis Rueda Canada	19	509 1.3×	289 2.1×	108 1.3×	42 0.6×	48 0.8×	122	1.1k
Xiong Liu United States	15	568 1.5×	88 0.6×	104 1.3×	47 0.7×	47 0.8×	47	1.1k
Jianping Hua United States	19	593 1.5×	437 3.2×	114 1.4×	40 0.6×	39 0.6×	54	1.5k
Xiaowei Xu China	18	349 0.9×	308 2.2×	263 3.2×	44 0.6×	60 1.0×	64	1.2k
Sabri Boughorbel Qatar	15	231 0.6×	496 3.6×	80 1.0×	80 1.2×	41 0.7×	46	1.6k
Balázs Bánfai Switzerland	7	441 1.2×	110 0.8×	28 0.3×	63 0.9×	28 0.5×	11	997
Mohammed Elanbari Qatar	4	167 0.4×	278 2.0×	69 0.8×	49 0.7×	25 0.4×	12	879
Juliane Siebourg‐Polster Switzerland	7	212 0.6×	116 0.8×	31 0.4×	74 1.1×	17 0.3×	16	789

Countries citing papers authored by Bernhard Steiert

Since Specialization

Citations

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

Fields of papers citing papers by Bernhard Steiert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bernhard Steiert

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

All Works

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15 of 15 papers shown

Steiert, Bernhard, et al.. (2025). Optimizing Early Ophthalmology Clinical Trials: Home OCT and Modeling Can Reduce Sample Size by 20% to 40%. Translational Vision Science & Technology. 14(9). 2–2.

Steiert, Bernhard, et al.. (2024). Low‐dimensional neural ordinary differential equations accounting for inter‐individual variability implemented in Monolix and NONMEM. CPT Pharmacometrics & Systems Pharmacology. 14(1). 5–16. 1 indexed citations

Tönsing, Christian, Bernhard Steiert, Jens Timmer, & Clemens Kreutz. (2023). Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models. PLoS Computational Biology. 19(9). e1011417–e1011417. 3 indexed citations

Parrott, Neil, et al.. (2022). Introduction of an artificial neural network–based method for concentration‐time predictions. CPT Pharmacometrics & Systems Pharmacology. 11(6). 745–754. 18 indexed citations

Badillo, Solveig, Balázs Bánfai, Fabian Birzele, et al.. (2020). An Introduction to Machine Learning. Clinical Pharmacology & Therapeutics. 107(4). 871–885. 503 indexed citations breakdown →

Steiert, Bernhard, Clemens Kreutz, Andreas Raue, & Jens Timmer. (2019). Recipes for Analysis of Molecular Networks Using the Data2Dynamics Modeling Environment. Methods in molecular biology. 1945. 341–362. 5 indexed citations

Sampattavanich, Somponnat, et al.. (2018). Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases. Cell Systems. 6(6). 664–678.e9. 33 indexed citations

Hutchinson, Lucy, Bernhard Steiert, Antoine Soubret, et al.. (2018). Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level. CPT Pharmacometrics & Systems Pharmacology. 8(3). 131–134. 39 indexed citations

Steiert, Bernhard, Sofia Depner, Andreas Raue, et al.. (2016). Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells. PLoS Computational Biology. 12(8). e1005049–e1005049. 30 indexed citations

10.

Maiwald, Tim, Helge Hass, Bernhard Steiert, et al.. (2016). Driving the Model to Its Limit: Profile Likelihood Based Model Reduction. PLoS ONE. 11(9). e0162366–e0162366. 68 indexed citations

11.

Steiert, Bernhard, Jens Timmer, & Clemens Kreutz. (2016). L 1 regularization facilitates detection of cell type-specific parameters in dynamical systems. Bioinformatics. 32(17). i718–i726. 17 indexed citations

12.

Karr, Jonathan R., Jeremy Zucker, Andreas Raue, et al.. (2015). Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS Computational Biology. 11(5). e1004096–e1004096. 51 indexed citations

13.

Raue, Andreas, Bernhard Steiert, Max Schelker, et al.. (2015). Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics. 31(21). 3558–3560. 153 indexed citations

14.

Meyer, Pablo, Thomas Cokelaer, Deepak Chandran, et al.. (2014). Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach. BMC Systems Biology. 8(1). 13–13. 44 indexed citations

15.

Steiert, Bernhard, Andreas Raue, Jens Timmer, & Clemens Kreutz. (2012). Experimental Design for Parameter Estimation of Gene Regulatory Networks. PLoS ONE. 7(7). e40052–e40052. 47 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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