Marc Strickert

2.9k citations
68 papers · 1.8k · h-index 21

Impact in

    • Plant Molecular Biology Research
    • Plant Stress Responses and Tolerance
    • Plant nutrient uptake and metabolism
    • Seed Germination and Physiology
    • Wheat and Barley Genetics and Pathology
    • Neural Networks and Applications

Papers in

Marc Strickert

62 papers receiving 1.7k citations

Peers

Marc Strickert
Comparison fields: 5 of 123
  • Plant Science 1.1k
  • Artificial Intelligence 381
  • Computer Vision and Pattern Recognition 239
  • Agronomy and Crop Science 96
  • Molecular Biology 632
Replace Wensheng Wang with:
Wensheng Wang China
Tongming Yin China
Julie Dickerson United States
Falk Schreiber Germany
Stefan Bleuler Switzerland
N. K. Gupta India
Lin Zhu China
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Citations per field
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Citations per year

Countries citing papers authored by Marc Strickert

Since Specialization
Citations

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

Fields of papers citing papers by Marc Strickert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Marc Strickert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Marc Strickert Line = papers co-authored together Marc Strickert links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008233
2 2011225
3 2006139
4 2019114
5 2009113
6 200591
7 200885
8 200475
9 200569
10 200555
11 200454
12 201345
13 201044
14 201143
15 201442
16 201731
17 202028
18 201223
19 201023
20
A sparse kernelized matrix learning vector quantization model for human activity recognition.
201322

About Marc Strickert

Marc Strickert is a scholar working on Artificial Intelligence, Molecular Biology, Plant Science, Computer Vision and Pattern Recognition and Genetics, having authored 68 papers that have together received 1.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (19 papers), Gene expression and cancer classification (13 papers), Face and Expression Recognition (7 papers), Plant nutrient uptake and metabolism (6 papers), Spectroscopy and Chemometric Analyses (6 papers), Genomics and Chromatin Dynamics (6 papers), Genetic Mapping and Diversity in Plants and Animals (6 papers) and Plant Molecular Biology Research (5 papers). The work is most often cited by research in Plant Science (1.1k citations), Artificial Intelligence (381 citations), Computer Vision and Pattern Recognition (239 citations), Agronomy and Crop Science (96 citations) and Molecular Biology (632 citations). Marc Strickert has collaborated with scholars based in Germany, Argentina and United Kingdom. Frequent co-authors include Barbara Hammer, Nese Sreenivasulu, Ulrich Wobus, Winfriede Weschke, Thomas Villmann, Volodymyr Radchuk, Uwe Scholz, Alessio Micheli, Alessandro Sperduti and Björn Usadel. Their work appears in journals such as Neurocomputing, PLANT PHYSIOLOGY, PLoS Computational Biology, Bioinformatics and BMC Bioinformatics.

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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