Lasse Folkersen

24.8k total citations · 2 hit papers
97 papers, 3.6k citations indexed

About

Lasse Folkersen is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Lasse Folkersen has authored 97 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 29 papers in Immunology and 27 papers in Genetics. Recurrent topics in Lasse Folkersen's work include Genetic Associations and Epidemiology (20 papers), Atherosclerosis and Cardiovascular Diseases (13 papers) and RNA modifications and cancer (6 papers). Lasse Folkersen is often cited by papers focused on Genetic Associations and Epidemiology (20 papers), Atherosclerosis and Cardiovascular Diseases (13 papers) and RNA modifications and cancer (6 papers). Lasse Folkersen collaborates with scholars based in Sweden, Denmark and United States. Lasse Folkersen's co-authors include Ulf Hedin, Anders Gabrielsen, Per Eriksson, Gabrielle Paulsson‐Berne, Göran K. Hansson, Anders Franco‐Cereceda, Anders Hamsten, Johan Ekstrand, Mariette Lengquist and Bente Halvorsen and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Lasse Folkersen

96 papers receiving 3.6k citations

Hit Papers

CD49a Expression Defines Tissue-Resident CD8 + T Cells Po... 2017 2026 2020 2023 2017 2022 100 200 300 400
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. CD49a Expression Defines Tissue-Resident CD8 + T Cells Poised for Cytotoxic Function in Human Skin
    2017 475 citations Lasse Folkersen et al. profile →
  2. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort
    2022 135 citations Lasse Folkersen et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lasse Folkersen Sweden 34 1.4k 1.2k 629 479 438 97 3.6k
Mukesh K. Jain United States 38 2.2k 1.5× 678 0.6× 392 0.6× 491 1.0× 619 1.4× 68 4.1k
Martin P. Playford United States 32 1.5k 1.1× 1.2k 1.0× 232 0.4× 388 0.8× 315 0.7× 102 3.8k
Mete Civelek United States 35 2.6k 1.8× 997 0.8× 703 1.1× 463 1.0× 854 1.9× 85 4.5k
Maria Pia Rastaldi Italy 43 2.6k 1.8× 1.4k 1.1× 709 1.1× 289 0.6× 462 1.1× 120 7.3k
Bruce A. Hug United States 23 1.6k 1.1× 590 0.5× 220 0.3× 502 1.0× 291 0.7× 47 3.6k
Saskia C.A. de Jager Netherlands 35 1.5k 1.1× 1.5k 1.3× 173 0.3× 778 1.6× 314 0.7× 129 4.0k
José J. Fuster Spain 31 1.6k 1.2× 882 0.7× 255 0.4× 927 1.9× 442 1.0× 65 4.7k
Yusuke Kobayashi Japan 31 1.3k 0.9× 525 0.4× 280 0.4× 165 0.3× 658 1.5× 232 3.4k
José Vilar France 38 2.1k 1.5× 1.2k 1.0× 151 0.2× 1.0k 2.1× 443 1.0× 89 4.3k
David Abraham United Kingdom 36 1.5k 1.0× 606 0.5× 287 0.5× 317 0.7× 255 0.6× 128 4.7k

Countries citing papers authored by Lasse Folkersen

Since Specialization
Citations

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

Fields of papers citing papers by Lasse Folkersen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lasse Folkersen

This figure shows the co-authorship network connecting the top 25 collaborators of Lasse Folkersen. A scholar is included among the top collaborators of Lasse Folkersen 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 Lasse Folkersen. Lasse Folkersen 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
1.
Garcia, M., Lasse Folkersen, Francesco Lescai, et al.. (2024). Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery. NAR Genomics and Bioinformatics. 6(2). lqae031–lqae031. 17 indexed citations
2.
Folkersen, Lasse, et al.. (2022). A qualitative study exploring the consumer experience of receiving self-initiated polygenic risk scores from a third-party website. European Journal of Human Genetics. 31(4). 424–429. 4 indexed citations
3.
Turner, Adam W., Paulina Lau, Per Eriksson, et al.. (2022). A novel anti-inflammatory role links the CARS2 locus to protection from coronary artery disease. Atherosclerosis. 348. 8–15. 10 indexed citations
4.
Yoosuf, Niyaz, Mateusz Maciejewski, Daniel Ziemek, et al.. (2021). Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis. Lara D. Veeken. 61(4). 1680–1689. 39 indexed citations
5.
Pain, Oliver, Kylie P. Glanville, Saskia P. Hagenaars, et al.. (2021). Imputed gene expression risk scores: a functionally informed component of polygenic risk. Human Molecular Genetics. 30(8). 727–738. 13 indexed citations
6.
Sun, Jiangming, Yunpeng Wang, Lasse Folkersen, et al.. (2021). Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. Nature Communications. 12(1). 5276–5276. 15 indexed citations
7.
Verouhis, Dinos, Felix Böhm, Per Eriksson, et al.. (2021). Arginase 1 is upregulated at admission in patients with ST‐elevation myocardial infarction. Journal of Internal Medicine. 290(5). 1061–1070. 9 indexed citations
8.
Folkersen, Lasse, et al.. (2021). Why do people seek out polygenic risk scores for complex disorders, and how do they understand and react to results?. European Journal of Human Genetics. 30(1). 81–87. 33 indexed citations
9.
Howard, David M., Lasse Folkersen, Jonathan R. I. Coleman, et al.. (2020). Genetic stratification of depression in UK Biobank. Translational Psychiatry. 10(1). 163–163. 17 indexed citations
10.
Díaz-Gallo, Lina-Marcela, Daniel Ramsköld, Klementy Shchetynsky, et al.. (2018). Systematic approach demonstrates enrichment of multiple interactions between non-HLA risk variants and HLA-DRB1 risk alleles in rheumatoid arthritis. Annals of the Rheumatic Diseases. 77(10). 1454–1462. 16 indexed citations
11.
Shchetynsky, Klementy, Lina-Marcela Díaz-Gallo, Lasse Folkersen, et al.. (2017). Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis. Arthritis Research & Therapy. 19(1). 19–19. 16 indexed citations
12.
Folkersen, Lasse, M Berg, Ljubica Perisic, et al.. (2016). LowTLR7gene expression in atherosclerotic plaques is associated with major adverse cardio- and cerebrovascular events. Cardiovascular Research. 113(1). 30–39. 30 indexed citations
13.
Folkersen, Lasse, Shameek Biswas, Klaus Stensgaard Frederiksen, et al.. (2015). Applying genetics in inflammatory disease drug discovery. Drug Discovery Today. 20(10). 1176–1181. 2 indexed citations
14.
Iglesias, María Jesús, Olof Emanuelsson, Bengt Sennblad, et al.. (2012). Correction: Combined Chromatin and Expression Analysis Reveals Specific Regulatory Mechanisms within Cytokine Genes in the Macrophage Early Immune Response. PLoS ONE. 7(4). 1 indexed citations
15.
Pilbrow, Anna P., Lasse Folkersen, John F. Pearson, et al.. (2012). The Chromosome 9p21.3 Coronary Heart Disease Risk Allele Is Associated with Altered Gene Expression in Normal Heart and Vascular Tissues. PLoS ONE. 7(6). e39574–e39574. 33 indexed citations
16.
Björck, Hanna M., Shohreh Maleki, Siv Nilsson, et al.. (2012). Characterization of Shear-Sensitive Genes in the Normal Rat Aorta Identifies Hand2 as a Major Flow-Responsive Transcription Factor. PLoS ONE. 7(12). e52227–e52227. 16 indexed citations
17.
Iglesias, María Jesús, Olof Emanuelsson, Bengt Sennblad, et al.. (2012). Combined Chromatin and Expression Analysis Reveals Specific Regulatory Mechanisms within Cytokine Genes in the Macrophage Early Immune Response. PLoS ONE. 7(2). e32306–e32306. 18 indexed citations
18.
Razuvaev, Anton, Johan Ekstrand, Lasse Folkersen, et al.. (2011). Correlations Between Clinical Variables and Gene-expression Profiles in Carotid Plaque Instability. European Journal of Vascular and Endovascular Surgery. 42(6). 722–730. 39 indexed citations
19.
Gertow, Karl, Elena Nobili, Lasse Folkersen, et al.. (2011). 12- and 15-lipoxygenases in human carotid atherosclerotic lesions: Associations with cerebrovascular symptoms. Atherosclerosis. 215(2). 411–416. 60 indexed citations
20.
Folkersen, Lasse, Ferdinand van’t Hooft, Ekaterina Chernogubova, et al.. (2010). Association of Genetic Risk Variants With Expression of Proximal Genes Identifies Novel Susceptibility Genes for Cardiovascular Disease. Circulation Cardiovascular Genetics. 3(4). 365–373. 63 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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