Mikael Sunnåker

1.1k citations
21 papers · 619 indexed · 1 hit paper · h-index 10

Mikael Sunnåker

20 papers receiving 611 citations

Hit Papers

Approximate Bayesian Computation20132026201720212013100200300

Peers

Mikael Sunnåker
Comparison fields: 5 of 132
  • Molecular Biology 210
  • Genetics 109
  • Artificial Intelligence 104
  • Statistics and Probability 80
  • Modeling and Simulation 51
Replace Jasmin Bachmann with:
Jasmin Bachmann Germany
Dennis Prangle United Kingdom
Claus Bendtsen United Kingdom
Nicholas A. James United States
Juho Piironen Finland
Kevin Flores United States
Oliver J. Maclaren New Zealand
W. Clayton Thompson United States
Arnab Maity United States
Adam Bobrowski Poland
Mikael Sunnåker relative to Jasmin Bachmann Germany Jasmin Bachmann's profile →
Citations per field
00.5×1.6×
Jasmin Bachmann · 1×
Citations per year

Countries citing papers authored by Mikael Sunnåker

Since Specialization
Citations

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

Fields of papers citing papers by Mikael Sunnåker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mikael Sunnåker

This figure shows the co-authorship network connecting the top 25 collaborators of Mikael Sunnåker. A scholar is included among the top collaborators of Mikael Sunnåker based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mikael Sunnåker. Mikael Sunnåker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 1
3 2
4 1
5 16
6 3
7 6
8 3
9 6
10 3
11 1
12 42
13 14
14
Approximate Bayesian Computationbreakdown →
378
15 40
16 30
17 11
18 27
19 17
20 13

About Mikael Sunnåker

Mikael Sunnåker is a scholar working on Nephrology, Internal Medicine and Endocrinology, Diabetes and Metabolism, having authored 21 papers that have together received 619 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (6 papers), Lung Cancer Treatments and Mutations (3 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Modeling and Simulation (51 citations), Statistics and Probability (80 citations) and Statistics, Probability and Uncertainty (40 citations). Mikael Sunnåker has collaborated with scholars based in Switzerland, Sweden and United States. Frequent co-authors include Alberto Giovanni Busetto, Christophe Dessimoz, Jukka Corander, Elina Numminen, Matthieu Foll, Mats Jirstrand, Gunnar Cedersund, Jörg Stelling, Joerg Stelling and Andreas Wagner. Their work appears in journals such as Journal of Clinical Oncology, Bioinformatics and BMC 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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