Sipko van Dam

2.5k total citations · 1 hit paper
17 papers, 1.0k citations indexed

About

Sipko van Dam is a scholar working on Molecular Biology, Rheumatology and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Sipko van Dam has authored 17 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Rheumatology and 4 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Sipko van Dam's work include Bioinformatics and Genomic Networks (5 papers), GDF15 and Related Biomarkers (3 papers) and RNA Research and Splicing (3 papers). Sipko van Dam is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), GDF15 and Related Biomarkers (3 papers) and RNA Research and Splicing (3 papers). Sipko van Dam collaborates with scholars based in Netherlands, United Kingdom and United States. Sipko van Dam's co-authors include João Pedro de Magalhães, Urmo Võsa, Adriaan van der Graaf, Lude Franke, Thomas Craig, Shona H. Wood, Anis Larbi, Gianni Monaco, Daniel Wuttke and Susan Clarke and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Scientific Reports.

In The Last Decade

Sipko van Dam

16 papers receiving 1.0k citations

Hit Papers

Gene co-expression analysis for functional classification... 2016 2026 2019 2022 2016 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Gene co-expression analysis for functional classification and gene–disease predictions
    2016 666 citations Sipko van Dam, Urmo Võsa et al. Briefings in Bioinformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sipko van Dam Netherlands 9 736 125 119 119 83 17 1.0k
Lina Wadi Canada 5 762 1.0× 113 0.9× 227 1.9× 85 0.7× 104 1.3× 5 1.3k
Robin Haw Canada 17 1.2k 1.6× 111 0.9× 101 0.8× 89 0.7× 194 2.3× 27 1.5k
Abhijeet R. Sonawane United States 14 635 0.9× 181 1.4× 100 0.8× 92 0.8× 31 0.4× 24 997
Andrew Gregg United States 7 905 1.2× 205 1.6× 49 0.4× 185 1.6× 50 0.6× 10 1.1k
Thomas Schmitt Germany 22 958 1.3× 173 1.4× 47 0.4× 228 1.9× 111 1.3× 35 1.4k
David Amar Israel 19 624 0.8× 197 1.6× 114 1.0× 116 1.0× 72 0.9× 46 1.2k
Yulin Dai United States 17 600 0.8× 260 2.1× 139 1.2× 118 1.0× 36 0.4× 55 1.0k
Nick Owens United Kingdom 20 931 1.3× 167 1.3× 91 0.8× 55 0.5× 129 1.6× 36 1.3k
Tova F Fuller United States 8 669 0.9× 225 1.8× 108 0.9× 113 0.9× 62 0.7× 8 1.0k
Amy Leonardson United States 9 662 0.9× 234 1.9× 58 0.5× 91 0.8× 61 0.7× 10 960

Countries citing papers authored by Sipko van Dam

Since Specialization
Citations

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

Fields of papers citing papers by Sipko van Dam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sipko van Dam. 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 Sipko van Dam. The network helps show where Sipko van Dam may publish in the future.

Co-authorship network of co-authors of Sipko van Dam

This figure shows the co-authorship network connecting the top 25 collaborators of Sipko van Dam. A scholar is included among the top collaborators of Sipko van Dam 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 Sipko van Dam. Sipko van Dam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Kokkorakis, Michail, Filippos Anagnostakis, Ronney Shantouf, et al.. (2024). Simplifying coronary artery disease risk stratification: development and validation of a questionnaire-based alternative comparable to clinical risk tools. EBioMedicine. 111. 105518–105518.
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Forte, José Castela, Sipko van Dam, Niels H. Chavannes, et al.. (2023). A Digitally Enabled Combined Lifestyle Intervention for Weight Loss: Pilot Study in a Dutch General Population Cohort. JMIR Formative Research. 8. e38891–e38891. 1 indexed citations
4.
Dam, Sipko van, et al.. (2023). The necessity of incorporating non-genetic risk factors into polygenic risk score models. Scientific Reports. 13(1). 1351–1351. 4 indexed citations
5.
Kokkorakis, Michail, Sipko van Dam, Shahrad Taheri, et al.. (2023). Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study. EClinicalMedicine. 64. 102235–102235. 8 indexed citations
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Forte, José Castela, et al.. (2022). Effect of a Digitally-Enabled, Preventive Health Program on Blood Pressure in an Adult, Dutch General Population Cohort: An Observational Pilot Study. International Journal of Environmental Research and Public Health. 19(7). 4171–4171. 2 indexed citations
9.
Tejada-Martínez, Daniela, et al.. (2021). Evolution, structure and emerging roles of C1ORF112 in DNA replication, DNA damage responses, and cancer. Cellular and Molecular Life Sciences. 78(9). 4365–4376. 11 indexed citations
10.
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Dam, Sipko van, Urmo Võsa, Adriaan van der Graaf, Lude Franke, & João Pedro de Magalhães. (2016). Gene co-expression analysis for functional classification and gene–disease predictions. Briefings in Bioinformatics. 19(4). bbw139–bbw139. 666 indexed citations breakdown →
12.
Monaco, Gianni, et al.. (2015). A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels. BMC Evolutionary Biology. 15(1). 259–259. 80 indexed citations
13.
Wood, Shona H., Sipko van Dam, Thomas Craig, et al.. (2015). Transcriptome analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging. Genome biology. 16(1). 285–285. 45 indexed citations
14.
Dam, Sipko van, Thomas Craig, & João Pedro de Magalhães. (2014). GeneFriends: a human RNA-seq-based gene and transcript co-expression database. Nucleic Acids Research. 43(D1). D1124–D1132. 85 indexed citations
15.
Plank, Michael, Daniel Wuttke, Sipko van Dam, Susan Clarke, & João Pedro de Magalhães. (2012). A meta-analysis of caloric restriction gene expression profiles to infer common signatures and regulatory mechanisms. Molecular BioSystems. 8(4). 1339–1349. 61 indexed citations
16.
Dam, Sipko van, et al.. (2012). GeneFriends: An online co-expression analysis tool to identify novel gene targets for aging and complex diseases. BMC Genomics. 13(1). 535–535. 52 indexed citations
17.
Silva, Ana Sofia, et al.. (2011). Gathering insights on disease etiology from gene expression profiles of healthy tissues. Bioinformatics. 27(23). 3300–3305. 15 indexed citations

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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