M. Schuller

1.2k citations
20 papers · 453 · h-index 12

Impact in

  • Physiology top 5%
    • Calcium signaling and nucleotide metabolism
  • Oncology top 10%
    • PARP inhibition in cancer therapy

Papers in

    • DNA Repair Mechanisms 5
    • RNA and protein synthesis mechanisms 3
    • Cancer therapeutics and mechanisms 2
    • CRISPR and Genetic Engineering 2
    • PARP inhibition in cancer therapy 14

M. Schuller

20 papers receiving 448 citations

Peers

M. Schuller
Comparison fields: 5 of 60
  • Physiology 73
  • Oncology 273
  • Immunology 107
  • Molecular Biology 226
  • Clinical Biochemistry 21
Replace Beata Wielgus‐Kutrowska with:
Beata Wielgus‐Kutrowska Poland
Carlos Madrid-Aliste United States
Nathalie Ulryck France
Simon Barkow‐Oesterreicher Switzerland
Mareike Bütepage Germany
Ilsa T. Kirby United States
Gulilat Gebeyehu United States
Jelena Melesina Germany
Alena Siarheyeva Canada
Darin Vanderpool United States
M. Schuller relative to Beata Wielgus‐Kutrowska Poland Beata Wielgus‐Kutrowska's profile →
Citations per field
00.5×10×15×
Beata Wielgus‐Kutrowska · 1×
Citations per year

Countries citing papers authored by M. Schuller

Since Specialization
Citations

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

Fields of papers citing papers by M. Schuller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside M. Schuller, 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 M. Schuller Line = papers co-authored together M. Schuller links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202156
2 202054
3 202347
4 202342
5 201538
6 202135
7 202330
8 202429
9 201729
10 201825
11 202414
12 201713
13 202311
14 202410
15 20226
16 20255
17 20254
18 20242
19 20252
20 20241

About M. Schuller

M. Schuller is a scholar working on Molecular Biology, Oncology, Immunology, Electrical and Electronic Engineering and Infectious Diseases, having authored 20 papers that have together received 453 indexed citations. Recurring topics across this work include PARP inhibition in cancer therapy (14 papers), Toxin Mechanisms and Immunotoxins (7 papers), DNA Repair Mechanisms (5 papers), Integrated Circuits and Semiconductor Failure Analysis (3 papers), RNA and protein synthesis mechanisms (3 papers), Cancer therapeutics and mechanisms (2 papers), Bacteriophages and microbial interactions (2 papers) and CRISPR and Genetic Engineering (2 papers). The work is most often cited by research in Physiology (73 citations), Oncology (273 citations), Immunology (107 citations), Molecular Biology (226 citations) and Clinical Biochemistry (21 citations). M. Schuller has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Ivan Ahel, Dragana Ahel, J.G.M. Rack, Valentina Zorzini, Zihan Zhu, A. Ariza, Jonathan M. Elkins, Shan Goh, Timothy D. W. Claridge and Andreja Mikoč. Their work appears in journals such as Nucleic Acids Research, Molecular Cell, The EMBO Journal, Nature Communications and Toxins.

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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