Maike Tech

917 citations
7 papers · 460 · h-index 7

Impact in

    • Genomics and Phylogenetic Studies
    • Machine Learning in Bioinformatics
    • RNA and protein synthesis mechanisms
    • Insect Resistance and Genetics
    • Gut microbiota and health
    • Gene expression and cancer classification

Papers in

    • Genomics and Phylogenetic Studies 5
    • Machine Learning in Bioinformatics 4
    • RNA and protein synthesis mechanisms 2
    • Microbial Metabolic Engineering and Bioproduction 1
    • Insect Resistance and Genetics 1

Maike Tech

7 papers receiving 447 citations

Peers

Maike Tech
Comparison fields: 5 of 70
  • Molecular Biology 368
  • Insect Science 44
  • Ecology 88
  • Plant Science 101
  • Cell Biology 41
Replace Linlin You with:
Linlin You China
Divya Sain United States
Nancy Ontiveros‐Palacios United Kingdom
Chengcang Wu United States
Anthony Bretaudeau France
Donna Saville Canada
Charles E. Bullerwell Canada
Orílio Leoncini Brazil
S. Tokishita Japan
Maike Tech relative to Linlin You China Linlin You's profile →
Citations per field
00.5×4.9×
Linlin You · 1×
Citations per year

Countries citing papers authored by Maike Tech

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 23 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.

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

All Works

7 of 7 papers shown
#Work
1 2014100
2 200980
3 201480
4 200376
5 200859
6 200445
7 200620

About Maike Tech

Maike Tech is a scholar working on Molecular Biology, Ecology, Cell Biology, Genetics and Insect Science, having authored 7 papers that have together received 460 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (5 papers), Machine Learning in Bioinformatics (4 papers), RNA and protein synthesis mechanisms (2 papers), Plant-Microbe Interactions and Immunity (1 paper), Insect Utilization and Effects (1 paper), Microbial Metabolic Engineering and Bioproduction (1 paper), Plant Pathogens and Fungal Diseases (1 paper) and Insect Resistance and Genetics (1 paper). The work is most often cited by research in Molecular Biology (368 citations), Insect Science (44 citations), Ecology (88 citations), Plant Science (101 citations) and Cell Biology (41 citations). Maike Tech has collaborated with scholars based in Germany, Vietnam and Spain. Frequent co-authors include Peter Meinicke, Rainer Merkl, Katharina J. Hoff, Thomas Lingner, Burkhard Morgenstern, Daniela Großmann, Jürgen Dönitz, Martin Klingler, Gregor Bucher and Christian Schmitt-Engel. Their work appears in journals such as BMC Bioinformatics, Nucleic Acids Research, 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.

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