Markus Reischl

6.3k total citations
186 papers, 3.3k citations indexed

About

Markus Reischl is a scholar working on Biomedical Engineering, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Markus Reischl has authored 186 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Biomedical Engineering, 37 papers in Molecular Biology and 30 papers in Artificial Intelligence. Recurrent topics in Markus Reischl's work include Cell Image Analysis Techniques (25 papers), Muscle activation and electromyography studies (22 papers) and Zebrafish Biomedical Research Applications (18 papers). Markus Reischl is often cited by papers focused on Cell Image Analysis Techniques (25 papers), Muscle activation and electromyography studies (22 papers) and Zebrafish Biomedical Research Applications (18 papers). Markus Reischl collaborates with scholars based in Germany, United Kingdom and United States. Markus Reischl's co-authors include Ralf Mikut, Rüdiger Rudolf, Christian Pylatiuk, Urban Liebel, Sven Perner, Muzamil Majid Khan, Uwe Strähle, Rüdiger Alshut, Ferenc Müller and Pavel A. Levkin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Markus Reischl

164 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Markus Reischl Germany 30 1.4k 497 496 290 284 186 3.3k
Jeong‐Soo Park South Korea 32 1.1k 0.8× 317 0.6× 381 0.8× 408 1.4× 211 0.7× 183 4.8k
Fons J. Verbeek Netherlands 32 2.2k 1.5× 250 0.5× 609 1.2× 255 0.9× 183 0.6× 153 4.6k
Aleš Hampl Czechia 35 2.8k 1.9× 601 1.2× 325 0.7× 188 0.6× 238 0.8× 159 4.6k
Yun Chen China 34 1.8k 1.2× 595 1.2× 636 1.3× 149 0.5× 217 0.8× 171 4.4k
Allen Goodman United States 12 1.4k 1.0× 297 0.6× 301 0.6× 148 0.5× 89 0.3× 18 3.1k
Jie Zhu China 36 2.1k 1.5× 662 1.3× 211 0.4× 264 0.9× 125 0.4× 216 5.0k
Wei Tang China 41 1.9k 1.4× 891 1.8× 209 0.4× 214 0.7× 321 1.1× 267 5.6k
Guang Jin China 38 1.5k 1.0× 233 0.5× 202 0.4× 193 0.7× 497 1.8× 274 4.5k
Nan Liu China 31 1.9k 1.3× 205 0.4× 270 0.5× 132 0.5× 82 0.3× 113 4.0k
Pengcheng Li China 39 986 0.7× 1.2k 2.4× 133 0.3× 685 2.4× 184 0.6× 498 6.2k

Countries citing papers authored by Markus Reischl

Since Specialization
Citations

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

Fields of papers citing papers by Markus Reischl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Markus Reischl

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Reischl. A scholar is included among the top collaborators of Markus Reischl 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 Markus Reischl. Markus Reischl 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.
Jeschull, Fabian, et al.. (2025). ML-Driven Contamination Classification for XPS Analysis of PLA Surfaces. Current Directions in Biomedical Engineering. 11(1). 330–333.
2.
Reischl, Markus, et al.. (2024). A review of adaptable conventional image processing pipelines and deep learning on limited datasets. Machine Vision and Applications. 35(2). 2 indexed citations
3.
Schweidler, Simon, et al.. (2024). Quantitative Convolutional Neural Network Based Multi-Phase XRD Pattern Analysis. SHILAP Revista de lepidopterología. 10(4). 307–310.
4.
Schweidler, Simon, Ling Lin, Kai Wang, et al.. (2024). Using the High-Entropy Approach to Obtain Multimetal Oxide Nanozymes: Library Synthesis, In Silico Structure–Activity, and Immunoassay Performance. ACS Nano. 18(29). 19024–19037. 8 indexed citations
5.
Geimer, Marcus, et al.. (2024). Adaptive Training for Robust Object Detection in Autonomous Driving Environments. IEEE Transactions on Intelligent Vehicles. 1–15. 2 indexed citations
6.
Cui, Haijun, Christel Herold‐Mende, Markus Reischl, et al.. (2023). Repurposing FDA‐Approved Drugs for Temozolomide‐Resistant IDH1 Mutant Glioma Using High‐Throughput Miniaturized Screening on Droplet Microarray Chip. Advanced Healthcare Materials. 12(24). e2300591–e2300591. 9 indexed citations
7.
Neumann, Oliver, Marcel Schilling, Markus Reischl, & Ralf Mikut. (2022). EasyMLServe: Easy Deployment of REST Machine Learning Services. 11–30.
8.
Liu, Yanxi, Sarah Bertels, Markus Reischl, et al.. (2022). Droplet Microarray Based Screening Identifies Proteins for Maintaining Pluripotency of hiPSCs. Advanced Healthcare Materials. 11(18). e2200718–e2200718. 10 indexed citations
9.
Lei, Wenxi, Anna A. Popova, Markus Reischl, et al.. (2022). Droplet Microarray as a Powerful Platform for Seeking New Antibiotics Against Multidrug‐Resistant Bacteria. Advanced Biology. 6(12). e2200166–e2200166. 10 indexed citations
10.
Scherr, Tim, et al.. (2022). Automated Annotator Variability Inspection for Biomedical Image Segmentation. IEEE Access. 10. 2753–2765. 12 indexed citations
11.
Reischl, Markus, et al.. (2021). Systematic assessment of the biocompatibility of materials for inkjet-printed ozone sensors for medical therapy. Flexible and Printed Electronics. 6(4). 43003–43003. 5 indexed citations
12.
Reischl, Markus, et al.. (2021). Genetic Algorithm for the Optimal LiDAR Sensor Configuration on a Vehicle. IEEE Sensors Journal. 22(3). 2735–2743. 12 indexed citations
13.
Reischl, Markus, et al.. (2021). Evaluation of four point cloud similarity measures for the use in autonomous driving. at - Automatisierungstechnik. 69(6). 499–510. 1 indexed citations
14.
Liu, Yanxi, et al.. (2020). High‐Throughput Screening of Cell Transfection Enhancers Using Miniaturized Droplet Microarrays. Advanced Biosystems. 4(3). e1900257–e1900257. 17 indexed citations
15.
Just, Steffen, et al.. (2019). Machine Learning Methods for Automated Quantification of Ventricular Dimensions. Zebrafish. 16(6). 542–545. 10 indexed citations
16.
Khan, Muzamil Majid, Danilo Lustrino, Wilian A. Silveira, et al.. (2016). Sympathetic innervation controls homeostasis of neuromuscular junctions in health and disease. Proceedings of the National Academy of Sciences. 113(3). 746–750. 127 indexed citations
17.
Gourain, Victor, Markus Reischl, Pierre Affaticati, et al.. (2016). A novel brain tumour model in zebrafish reveals the role of YAP activation in MAPK/PI3K induced malignant growth. Disease Models & Mechanisms. 10(1). 15–28. 54 indexed citations
18.
Pylatiuk, Christian, et al.. (2014). Automatic Zebrafish Heartbeat Detection and Analysis for Zebrafish Embryos. Zebrafish. 11(4). 379–383. 51 indexed citations
19.
Wittmann, Christine, et al.. (2012). Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish. Journal of Visualized Experiments. e4203–e4203. 33 indexed citations
20.
Wittmann, Christine, et al.. (2012). Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish. Journal of Visualized Experiments. 17 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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