Gabriel Ricolleau

2.1k total citations
18 papers, 1.1k citations indexed

About

Gabriel Ricolleau is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Gabriel Ricolleau has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Oncology and 7 papers in Cancer Research. Recurrent topics in Gabriel Ricolleau's work include Ovarian cancer diagnosis and treatment (5 papers), Lymphoma Diagnosis and Treatment (4 papers) and Breast Cancer Treatment Studies (3 papers). Gabriel Ricolleau is often cited by papers focused on Ovarian cancer diagnosis and treatment (5 papers), Lymphoma Diagnosis and Treatment (4 papers) and Breast Cancer Treatment Studies (3 papers). Gabriel Ricolleau collaborates with scholars based in France, United States and Italy. Gabriel Ricolleau's co-authors include L. Campion, Mario Campone, Catherine Guérin‐Charbonnel, Wilfried Gouraud, Pascal Jézéquel, Christophe Leux, Jean‐Sébastien Frenel, Delphine Loussouarn, A Daver and P. Fumoleau and has published in prestigious journals such as Journal of Clinical Oncology, British Journal of Cancer and Annals of Oncology.

In The Last Decade

Gabriel Ricolleau

17 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gabriel Ricolleau France 14 626 402 344 200 111 18 1.1k
Nikita Makretsov Canada 14 682 1.1× 593 1.5× 380 1.1× 164 0.8× 59 0.5× 19 1.4k
PA Futreal United Kingdom 9 1.0k 1.6× 488 1.2× 486 1.4× 208 1.0× 129 1.2× 13 1.6k
Liu‐Ying Luo Canada 19 514 0.8× 426 1.1× 235 0.7× 156 0.8× 100 0.9× 22 1.5k
Karen G. Montgomery Australia 15 1.1k 1.8× 528 1.3× 362 1.1× 397 2.0× 143 1.3× 17 1.6k
Sarah E. Russell Australia 5 662 1.1× 385 1.0× 188 0.5× 197 1.0× 139 1.3× 5 1.0k
Andrea P. Myers United States 17 788 1.3× 299 0.7× 294 0.9× 202 1.0× 212 1.9× 36 1.3k
Sam Leung Canada 8 332 0.5× 238 0.6× 244 0.7× 214 1.1× 97 0.9× 13 808
Françoise Descôtes France 20 593 0.9× 342 0.9× 326 0.9× 154 0.8× 59 0.5× 66 1.2k
Philippe Haas Switzerland 8 588 0.9× 578 1.4× 207 0.6× 230 1.1× 36 0.3× 8 1.1k
Julia Dorn Germany 23 650 1.0× 634 1.6× 401 1.2× 110 0.6× 120 1.1× 57 1.5k

Countries citing papers authored by Gabriel Ricolleau

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel Ricolleau

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gabriel Ricolleau

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

All Works

18 of 18 papers shown
1.
Frenel, Jean‐Sébastien, L. Campion, Catherine Guérin‐Charbonnel, et al.. (2013). bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses. Database. 2013(0). bas060–bas060. 207 indexed citations
2.
Jézéquel, Pascal, L. Campion, Frédérique Spyratos, et al.. (2011). Validation of tumor‐associated macrophage ferritin light chain as a prognostic biomarker in node‐negative breast cancer tumors: A multicentric 2004 national PHRC study. International Journal of Cancer. 131(2). 426–437. 64 indexed citations
3.
Jézéquel, Pascal, Mario Campone, Wilfried Gouraud, et al.. (2011). bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer. Breast Cancer Research and Treatment. 131(3). 765–775. 308 indexed citations
4.
Loussouarn, Delphine, L. Campion, Mario Campone, et al.. (2009). Validation of UBE2C protein as a prognostic marker in node-positive breast cancer. British Journal of Cancer. 101(1). 166–173. 52 indexed citations
5.
Chérel, Michel, L. Campion, Stéphane Bezieau, et al.. (2009). Molecular screening of interleukin-6 gene promoter and influence of −174G/C polymorphism on breast cancer. Cytokine. 47(3). 214–223. 24 indexed citations
6.
Jézéquel, Pascal, Mario Campone, Henri Roché, et al.. (2008). A 38-gene expression signature to predict metastasis risk in node-positive breast cancer after systemic adjuvant chemotherapy: a genomic substudy of PACS01 clinical trial. Breast Cancer Research and Treatment. 116(3). 509–520. 11 indexed citations
7.
Bonnetain, Franck, et al.. (2007). Change in CA 125 levels after the first cycle of induction chemotherapy is an independent predictor of epithelial ovarian tumour outcome. Annals of Oncology. 18(5). 881–885. 30 indexed citations
8.
Riedinger, Jean‐Marc, Gabriel Ricolleau, N. Eche, et al.. (2006). CA 125 half-life and CA 125 nadir during induction chemotherapy are independent predictors of epithelial ovarian cancer outcome: results of a French multicentric study. Annals of Oncology. 17(8). 1234–1238. 82 indexed citations
9.
10.
Riedinger, Jean‐Marc, et al.. (2006). Biologie: décroissance du CA-125. A-t-on des indications de pilotage sur les traitements?. ONCOLOGIE. 8(2). 132–138.
11.
Jézéquel, Pascal, L. Campion, F. Dravet, et al.. (2004). G388R mutation of the FGFR4 gene is not relevant to breast cancer prognosis. British Journal of Cancer. 90(1). 189–193. 47 indexed citations
12.
Broët, Philippe, S Romain, A Daver, et al.. (2001). Thymidine Kinase as a Proliferative Marker: Clinical Relevance in 1,692 Primary Breast Cancer Patients. Journal of Clinical Oncology. 19(11). 2778–2787. 77 indexed citations
13.
Broët, Philippe, F. Spyratos, S Romain, et al.. (1999). Prognostic value of uPA and p53 accumulation measured by quantitative biochemical assays in 1245 primary breast cancer patients: a multicentre study. British Journal of Cancer. 80(3-4). 536–545. 25 indexed citations
14.
Koscielny, Serge, P. Terrier, A Daver, et al.. (1998). Quantitative determination of c-erbB-2 in human breast tumours: potential prognostic significance of low values. European Journal of Cancer. 34(4). 476–481. 22 indexed citations
15.
Bischof, P., et al.. (1995). Clinical validation of the new ELSA‐CA 125 II assay: Report of a european multicentre evaluation. International Journal of Cancer. 60(2). 199–203. 5 indexed citations
16.
Daver, A, et al.. (1990). [Diagnostic value of SCC-TA4 determination in 4 localizations of epidermoid cancers. An experience of the FNCLCC subgroup of radio-analysis].. PubMed. 77(8). 781–92. 9 indexed citations
17.
Ricolleau, Gabriel, et al.. (1984). Radioimmunoassay of the CA 12 5 antigen in ovarian carcinomas: advantages compared with CA 19-9 and CEA.. PubMed. 5(3-4). 151–9. 34 indexed citations
18.
Ricolleau, Gabriel, et al.. (1981). Use of serial carcinoembryonic antigen assays in detecting relapses in breast cancer involving high risk of metastasis. European Journal of Cancer (1965). 17(2). 233–238. 22 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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