Daniela Wieser

1.8k citations
14 papers · 449 · h-index 10

Impact in

Papers in

    • Bioinformatics and Genomic Networks 4
    • Genomics and Phylogenetic Studies 3
    • Gene expression and cancer classification 2
    • Machine Learning in Bioinformatics 2
    • Protein Structure and Dynamics 2
    • Genetics, Aging, and Longevity in Model Organisms 4

Daniela Wieser

14 papers receiving 439 citations

Peers

Daniela Wieser
Comparison fields: 5 of 85
  • Aging 68
  • Endocrine and Autonomic Systems 21
  • Molecular Biology 223
  • Computational Theory and Mathematics 48
  • Public Health, Environmental and Occupational Health 73
Replace Laurence Coquin with:
Laurence Coquin United States
Svetlana Pacifico United States
Michael E. Gruidl United States
David DeCaprio United States
Kurt D. Dittmar United States
Emmett Sprecher United States
Tiago Grego Portugal
Guillaume Diss Canada
Harshil Shah United States
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Citations per field
00.5×5.3×
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Citations per year

Countries citing papers authored by Daniela Wieser

Since Specialization
Citations

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

Fields of papers citing papers by Daniela Wieser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

14 of 14 papers shown
#Work
1 201673
2 201070
3 200962
4 201647
5 202044
6 200534
7 201029
8 200828
9 200523
10 200420
11 20129
12 20096
13 20232
14 20202

About Daniela Wieser

Daniela Wieser is a scholar working on Molecular Biology, Aging, Epidemiology, Public Health, Environmental and Occupational Health and Cellular and Molecular Neuroscience, having authored 14 papers that have together received 449 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (4 papers), Genetics, Aging, and Longevity in Model Organisms (4 papers), Genomics and Phylogenetic Studies (3 papers), Gene expression and cancer classification (2 papers), Machine Learning in Bioinformatics (2 papers), Protein Structure and Dynamics (2 papers), Parasites and Host Interactions (1 paper) and Trypanosoma species research and implications (1 paper). The work is most often cited by research in Aging (68 citations), Endocrine and Autonomic Systems (21 citations), Molecular Biology (223 citations), Computational Theory and Mathematics (48 citations) and Public Health, Environmental and Occupational Health (73 citations). Daniela Wieser has collaborated with scholars based in United Kingdom, Switzerland and United States. Frequent co-authors include Rolf Apweiler, Alex A. Freitas, Janet M. Thornton, Linda Partridge, Nazif Alic, A. Reghan Foley, Matthias Ziehm, Robert Petryszak, Irene Papatheodorou and Ernst Hafen. Their work appears in journals such as Bioinformatics, PLoS Genetics, In Silico Biology, Computer applications in the biosciences and IEEE/ACM Transactions on Computational Biology and 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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