Maike Tech
Impact in
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies
- Insect Resistance and Genetics
- Developmental Biology and Gene Regulation
- Machine Learning in Bioinformatics
- Fungal and yeast genetics research
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- Plant Pathogens and Fungal Diseases
Papers in
-
- Genomics and Phylogenetic Studies 8
- RNA and protein synthesis mechanisms 2
- Insect Resistance and Genetics 1
- Fungal and yeast genetics research 1
- Biomedical Text Mining and Ontologies 1
- Machine Learning in Bioinformatics 1
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- Neurobiology and Insect Physiology Research 2
- Co-authors
- Peter Meinicke (6 shared papers)Rainer Merkl (2 shared papers)Katharina J. Hoff (3 shared papers)Thomas Lingner (2 shared papers)Burkhard Morgenstern (4 shared papers)Jürgen Dönitz (1 shared paper)Christian Schmitt-Engel (1 shared paper)Daniela Großmann (1 shared paper)
In The Last Decade
Maike Tech
10 papers receiving 676 citations
Peers
Comparison fields: 5 of 59
- Molecular Biology 531
- Cell Biology 59
- Cellular and Molecular Neuroscience 68
- Insect Science 24
- Ecology 44
Countries citing papers authored by Maike Tech
This map shows the geographic impact of Maike Tech'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 Maike Tech with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maike Tech more than expected).
Fields of papers citing papers by Maike Tech
This network shows the impact of papers produced by Maike Tech. 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 Maike Tech. The network helps show where Maike Tech may publish in the future.
Co-authors
The 24 scholars most cited alongside Maike Tech, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 129 | |
| 2 | 2014 | 112 | |
| 3 | 2009 | 100 | |
| 4 | 2003 | 88 | |
| 5 | 2014 | 84 | |
| 6 | 2008 | 75 | |
| 7 | 2004 | 48 | |
| 8 | 2005 | 24 | |
| 9 | 2006 | 23 | |
| 10 | 2006 | 9 |
About Maike Tech
Maike Tech is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Cell Biology, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 10 papers that have together received 692 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (8 papers), RNA and protein synthesis mechanisms (2 papers), Neurobiology and Insect Physiology Research (2 papers), Insect Resistance and Genetics (1 paper), Fungal and yeast genetics research (1 paper), Biomedical Text Mining and Ontologies (1 paper), Machine Learning in Bioinformatics (1 paper) and Plant Pathogens and Fungal Diseases (1 paper). The work is most often cited by research in Molecular Biology (531 citations), Cell Biology (59 citations), Cellular and Molecular Neuroscience (68 citations), Insect Science (24 citations) and Ecology (44 citations). Maike Tech has collaborated with scholars based in Germany, Spain and Vietnam. Frequent co-authors include Peter Meinicke, Rainer Merkl, Katharina J. Hoff, Thomas Lingner, Burkhard Morgenstern, Jürgen Dönitz, Christian Schmitt-Engel, Daniela Großmann, Lizzy Gerischer and Michael Schoppmeier. Their work appears in journals such as BMC Bioinformatics, Nucleic Acids Research, Computer applications in the biosciences, New Phytologist and In Silico Biology.
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.