Trevor Clancy

1.5k total citations
37 papers, 891 citations indexed

About

Trevor Clancy is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Trevor Clancy has authored 37 papers receiving a total of 891 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 20 papers in Immunology and 8 papers in Oncology. Recurrent topics in Trevor Clancy's work include Immunotherapy and Immune Responses (12 papers), T-cell and B-cell Immunology (9 papers) and Bioinformatics and Genomic Networks (8 papers). Trevor Clancy is often cited by papers focused on Immunotherapy and Immune Responses (12 papers), T-cell and B-cell Immunology (9 papers) and Bioinformatics and Genomic Networks (8 papers). Trevor Clancy collaborates with scholars based in Norway, Sweden and United States. Trevor Clancy's co-authors include Eivind Hovig, Richard Stratford, Karl‐Johan Malmberg, Irantzu Anzar, Marie Sofie Yoo Larsen, Viola Nähse, Petra Groth, Randi G. Syljuåsen, Thomas Helleday and Claus Storgaard Sørensen and has published in prestigious journals such as The Journal of Cell Biology, Nature Immunology and Bioinformatics.

In The Last Decade

Trevor Clancy

36 papers receiving 876 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Trevor Clancy Norway 18 439 358 326 112 99 37 891
Christine Schweitzer Germany 14 276 0.6× 155 0.4× 254 0.8× 120 1.1× 81 0.8× 18 924
Inge Verbrugge Netherlands 17 555 1.3× 443 1.2× 621 1.9× 122 1.1× 98 1.0× 23 1.1k
Kelly M. Ramsbottom Australia 16 414 0.9× 654 1.8× 462 1.4× 57 0.5× 146 1.5× 22 1.0k
Shigeto Kawai Japan 14 339 0.8× 281 0.8× 188 0.6× 67 0.6× 152 1.5× 28 837
Silvia Crescioli United Kingdom 17 625 1.4× 450 1.3× 464 1.4× 52 0.5× 33 0.3× 33 1.3k
Caroline Bret France 13 455 1.0× 316 0.9× 162 0.5× 81 0.7× 156 1.6× 34 915
Wadie D. Mahauad‐Fernandez United States 11 583 1.3× 186 0.5× 248 0.8× 191 1.7× 34 0.3× 19 931
Tahlita C.M. Zuiverloon Netherlands 22 528 1.2× 300 0.8× 231 0.7× 161 1.4× 43 0.4× 55 1.4k
Oliver Feyen Germany 16 250 0.6× 460 1.3× 176 0.5× 80 0.7× 118 1.2× 32 841
Nathan D. Mathewson United States 15 398 0.9× 372 1.0× 240 0.7× 132 1.2× 186 1.9× 33 805

Countries citing papers authored by Trevor Clancy

Since Specialization
Citations

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

Fields of papers citing papers by Trevor Clancy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Trevor Clancy

This figure shows the co-authorship network connecting the top 25 collaborators of Trevor Clancy. A scholar is included among the top collaborators of Trevor Clancy 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 Trevor Clancy. Trevor Clancy 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.
Anzar, Irantzu, et al.. (2024). Deep learning of antibody epitopes using positional permutation vectors. Computational and Structural Biotechnology Journal. 23. 2695–2707.
2.
Pfefferle, Aline, Jodie P. Goodridge, Ebba Sohlberg, et al.. (2024). Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping. Nature Immunology. 25(8). 1445–1459. 28 indexed citations
3.
O’Day, Steven, et al.. (2023). Clinical Activity of Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: Results from a Phase I Trial. Clinical Cancer Research. 29(16). 3026–3036. 18 indexed citations
4.
Federico, Lorenzo, Simen Tennøe, Viktoriia Chaban, et al.. (2023). Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Frontiers in Immunology. 14. 1265044–1265044. 8 indexed citations
5.
Anzar, Irantzu, Pubudu Samarakoon, Leonardo A. Meza‐Zepeda, et al.. (2023). The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition. Frontiers in Immunology. 14. 1226445–1226445. 3 indexed citations
6.
Federico, Lorenzo, Tor Henrik Anderson Tvedt, Viktoriia Chaban, et al.. (2023). Robust spike-specific CD4+ and CD8+ T cell responses in SARS-CoV-2 vaccinated hematopoietic cell transplantation recipients: a prospective, cohort study. Frontiers in Immunology. 14. 1210899–1210899. 3 indexed citations
7.
Bounova, Gergana, Irantzu Anzar, Donjetë Simnica, et al.. (2022). Characterization of the T cell receptor repertoire and melanoma tumor microenvironment upon combined treatment with ipilimumab and hTERT vaccination. Journal of Translational Medicine. 20(1). 419–419. 16 indexed citations
8.
Cheng, Jun, et al.. (2020). Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Scientific Reports. 10(1). 22375–22375. 59 indexed citations
9.
Anzar, Irantzu, et al.. (2019). NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer. BMC Medical Genomics. 12(1). 63–63. 31 indexed citations
10.
Pfefferle, Aline, Bénédikt Jacobs, Eivind Heggernes Ask, et al.. (2019). Intra-lineage Plasticity and Functional Reprogramming Maintain Natural Killer Cell Repertoire Diversity. Cell Reports. 29(8). 2284–2294.e4. 37 indexed citations
11.
Björklund, Andreas T., Mattias Carlsten, Ebba Sohlberg, et al.. (2018). Complete Remission with Reduction of High-Risk Clones following Haploidentical NK-Cell Therapy against MDS and AML. Clinical Cancer Research. 24(8). 1834–1844. 141 indexed citations
12.
Dørum, Siri, Trevor Clancy, Henrik M. Reims, et al.. (2018). Characterization of the Small Intestinal Lesion in Celiac Disease by Label-Free Quantitative Mass Spectrometry. American Journal Of Pathology. 188(7). 1563–1579. 13 indexed citations
13.
Ragnum, Harald Bull, Cathinka Halle, Andrée Yeramian, et al.. (2016). Hypoxia-independent gene expression signature associated with radiosensitisation of prostate cancer cell lines by histone deacetylase inhibition. British Journal of Cancer. 115(8). 929–939. 24 indexed citations
14.
Nygård, Ståle, Trond Reitan, Trevor Clancy, et al.. (2014). Identifying pathogenic processes by integrating microarray data with prior knowledge. BMC Bioinformatics. 15(1). 115–115. 1 indexed citations
15.
16.
Halle, Cathinka, Malin Lando, Debbie Hege Svendsrud, et al.. (2011). Membranous Expression of Ectodomain Isoforms of the Epidermal Growth Factor Receptor Predicts Outcome after Chemoradiotherapy of Lymph Node–Negative Cervical Cancer. Clinical Cancer Research. 17(16). 5501–5512. 17 indexed citations
17.
Sandve, Geir Kjetil, Sveinung Gundersen, Halfdan Rydbeck, et al.. (2011). The differential disease regulome. BMC Genomics. 12(1). 353–353. 8 indexed citations
18.
Nähse, Viola, Marie Sofie Yoo Larsen, Petra Groth, et al.. (2010). Regulators of cyclin-dependent kinases are crucial for maintaining genome integrity in S phase. The Journal of Cell Biology. 188(5). 629–638. 138 indexed citations
19.
Pedicini, Marco, Fredrik Barrenäs, Trevor Clancy, et al.. (2010). Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation. PLoS Computational Biology. 6(12). e1001032–e1001032. 15 indexed citations
20.

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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2026