Carl Wibom

1.4k total citations
31 papers, 507 citations indexed

About

Carl Wibom is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Carl Wibom has authored 31 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 14 papers in Genetics and 11 papers in Cancer Research. Recurrent topics in Carl Wibom's work include Glioma Diagnosis and Treatment (11 papers), Cancer, Hypoxia, and Metabolism (4 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Carl Wibom is often cited by papers focused on Glioma Diagnosis and Treatment (11 papers), Cancer, Hypoxia, and Metabolism (4 papers) and Metabolomics and Mass Spectrometry Studies (4 papers). Carl Wibom collaborates with scholars based in Sweden, Norway and Denmark. Carl Wibom's co-authors include Henrik Antti, A. Tommy Bergenheim, Lina Mörén, Ulrika Andersson, Beatrice Melin, Per Bergström, Thomas Brännström, Mikael Johansson, Anna M. Dahlin and Roger Henriksson and has published in prestigious journals such as Blood, PLoS ONE and Cancer Research.

In The Last Decade

Carl Wibom

30 papers receiving 500 citations

Peers

Carl Wibom

GENET

EPIDE

ONCOL

Yunyan Wang China

Marisa Pautasso Italy

Hongguang Huang China

Megha S. Uppin India

Chaim B. Colen United States

Emilie‐Fleur Gautier France

Shyamala Navada United States

Britt Hanson United Kingdom

Aaron J. Knox United States

K. Bélanger Canada

Yunyan Wang China

Carl Wibom

330 ×1.2

53 ×0.3

GENET

238 ×1.4

41 ×0.6

EPIDE

72 ×1.3

ONCOL

Citations per year, relative to Carl Wibom Carl Wibom (= 1×) peers Yunyan Wang

Countries citing papers authored by Carl Wibom

Since Specialization

Citations

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

Fields of papers citing papers by Carl Wibom

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Carl Wibom

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

All Works

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20 of 20 papers shown

Wibom, Carl, Esmeralda Krop, Hilde Langseth, et al.. (2024). Decline in TGF-α Levels As a Predictor of Multiple Myeloma Progression: A Prospective Longitudinal Study Using the Janus Serum Bank. Blood. 144(Supplement 1). 6809–6809. 1 indexed citations

Nethander, Maria, Eivind Coward, Ene Reimann, et al.. (2022). Assessment of the genetic and clinical determinants of hip fracture risk: Genome-wide association and Mendelian randomization study. Cell Reports Medicine. 3(10). 100776–100776. 21 indexed citations

Svensson, Daniel, Andreas Sjödin, Pall I. Olason, et al.. (2019). A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis. PLoS ONE. 14(3). e0213350–e0213350. 6 indexed citations

Wibom, Carl, Esmeralda Krop, Ann-Sofie Johansson, et al.. (2019). Immune marker changes and risk of multiple myeloma: a nested case-control study using repeated pre-diagnostic blood samples. Haematologica. 104(12). 2456–2464. 10 indexed citations

Wu, Wendy, Gunnar Johansson, Carl Wibom, et al.. (2019). The Genetic Architecture of Gliomagenesis–Genetic Risk Variants Linked to Specific Molecular Subtypes. Cancers. 11(12). 2001–2001. 10 indexed citations

Andersson, Ulrika, Sofie Degerman, Anna M. Dahlin, et al.. (2018). The association between longer relative leukocyte telomere length and risk of glioma is independent of the potentially confounding factors allergy, BMI, and smoking. Cancer Causes & Control. 30(2). 177–185. 7 indexed citations

Wibom, Carl, et al.. (2017). Biomarker Dynamics in B-cell Lymphoma: A Longitudinal Prospective Study of Plasma Samples Up to 25 Years before Diagnosis. Cancer Research. 77(6). 1408–1415. 13 indexed citations

Wibom, Carl, et al.. (2016). Genetic risk variants in the CDKN2A/B, RTEL1 and EGFR genes are associated with somatic biomarkers in glioma. Journal of Neuro-Oncology. 127(3). 483–492. 28 indexed citations

Andersson, Ulrika, Anna M. Dahlin, Hilde Langseth, et al.. (2016). Pre-diagnostic serum levels of EGFR and ErbB2 and genetic glioma risk variants: a nested case-control study. Tumor Biology. 37(8). 11065–11072. 7 indexed citations

10.

Mörén, Lina, Carl Wibom, Per Bergström, et al.. (2016). Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas. Radiation Oncology. 11(1). 51–51. 29 indexed citations

11.

Wibom, Carl, Anna M. Dahlin, Hilde Langseth, et al.. (2015). Investigation of Established Genetic Risk Variants for Glioma in Prediagnostic Samples from a Population-Based Nested Case–Control Study. Cancer Epidemiology Biomarkers & Prevention. 24(5). 810–816. 15 indexed citations

12.

Dahlin, Anna M., Mads V. Hollegaard, Carl Wibom, et al.. (2015). CCND2, CTNNB1, DDX3X, GLI2, SMARCA4, MYC, MYCN, PTCH1, TP53, and MLL2 gene variants and risk of childhood medulloblastoma. Journal of Neuro-Oncology. 125(1). 75–78. 14 indexed citations

13.

Wibom, Carl. (2013). Protein Expression Profiling of Brain Tumor Tissue Using SELDI-MS. Methods in molecular biology. 1023. 191–202. 3 indexed citations

14.

Wibom, Carl, Peter Van Loo, Thomas Brännström, et al.. (2012). EGFR Gene Variants Are Associated with Specific Somatic Aberrations in Glioma. PLoS ONE. 7(12). e47929–e47929. 12 indexed citations

15.

Wibom, Carl, Roger Henriksson, Thomas Brännström, et al.. (2011). DNA-repair gene variants are associated with glioblastoma survival. Acta Oncologica. 51(3). 325–332. 12 indexed citations

16.

Berntsson, Shala Ghaderi, Carl Wibom, Roger Henriksson, et al.. (2011). Analysis of DNA repair gene polymorphisms and survival in low-grade and anaplastic gliomas. Journal of Neuro-Oncology. 105(3). 531–538. 4 indexed citations

17.

Sjöström, Stefan, Carl Wibom, Ulrika Andersson, et al.. (2010). Genetic variations in VEGF and VEGFR2 and glioblastoma outcome. Journal of Neuro-Oncology. 104(2). 523–527. 23 indexed citations

18.

Wibom, Carl, Lina Mörén, Mads Aarhus, et al.. (2009). Proteomic profiles differ between bone invasive and noninvasive benign meningiomas of fibrous and meningothelial subtype. Journal of Neuro-Oncology. 94(3). 321–331. 17 indexed citations

19.

Wibom, Carl, et al.. (2008). Cutaneous and mucosal human papillomaviruses differ in net surface charge, potential impact on tropism. Virology Journal. 5(1). 118–118. 31 indexed citations

20.

Wibom, Carl, Fredrik Pettersson, Michael Sjöstróm, et al.. (2006). Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment. British Journal of Cancer. 94(12). 1853–1863. 14 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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