Nicolas Wicker

446 citations
24 papers · 295 · h-index 9

Impact in

Papers in

    • Bioinformatics and Genomic Networks 4
    • Gene expression and cancer classification 4
    • Genomics and Phylogenetic Studies 3
    • Bayesian Methods and Mixture Models 7
    • Advanced Clustering Algorithms Research 3

Nicolas Wicker

23 papers receiving 285 citations

Peers

Nicolas Wicker
Comparison fields: 5 of 96
  • Statistics and Probability 22
  • Artificial Intelligence 76
  • Molecular Biology 156
  • Genetics 43
  • Signal Processing 16
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Citations per field
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Citations per year

Countries citing papers authored by Nicolas Wicker

Since Specialization
Citations

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

Fields of papers citing papers by Nicolas Wicker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200162
2 201249
3 200731
4 200728
5 200727
6 200223
7 200612
8 200410
9 20119
10 20127
11 20096
12 20136
13 20146
14 20125
15 20034
16 20113
17 20211
18 20101
19 20101
20 20171

About Nicolas Wicker

Nicolas Wicker is a scholar working on Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition, Geometry and Topology and Computational Theory and Mathematics, having authored 24 papers that have together received 295 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (7 papers), Bioinformatics and Genomic Networks (4 papers), Gene expression and cancer classification (4 papers), Genomics and Phylogenetic Studies (3 papers), Topological and Geometric Data Analysis (3 papers), Markov Chains and Monte Carlo Methods (3 papers), Advanced Clustering Algorithms Research (3 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Statistics and Probability (22 citations), Artificial Intelligence (76 citations), Molecular Biology (156 citations), Genetics (43 citations) and Signal Processing (16 citations). Nicolas Wicker has collaborated with scholars based in France, Japan and Canada. Frequent co-authors include Olivier Poch, Jean Muller, Ravi Kiran Reddy Kalathur, Hiroshi Mamitsuka, Frédéric Vivien, Canh Hao Nguyen, Andreas Binder, Benjamin Linard, Raymond Ripp and Hoan Nguyen. Their work appears in journals such as Nucleic Acids Research, Computational Statistics & Data Analysis, Computational Geometry, Statistics and Computing and Journal of Computational and Graphical Statistics.

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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