David Gacquer

1.3k citations
19 papers · 815 · h-index 11

Impact in

Papers in

David Gacquer

17 papers receiving 803 citations

Peers

David Gacquer
Comparison fields: 5 of 93
  • Developmental Neuroscience 83
  • Cancer Research 145
  • Molecular Biology 552
  • Aging 12
  • Endocrinology, Diabetes and Metabolism 93
Replace Philip Brennecke with:
Philip Brennecke Germany
Xiaomei Xu China
Melissa Hancock United States
Topi A. Tervonen Finland
Daniel G. Pankratz United States
Taehwan Shin United States
Ena Ladi United States
Siavash Fazel Darbandi United States
Abhijeet Pataskar Germany
Rongxin Fang United States
David Gacquer relative to Philip Brennecke Germany Philip Brennecke's profile →
Citations per field
00.5×2.6×
Philip Brennecke · 1×
Citations per year

Countries citing papers authored by David Gacquer

Since Specialization
Citations

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

Fields of papers citing papers by David Gacquer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside David Gacquer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Gacquer Line = papers co-authored together David Gacquer links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2018273
2 2015174
3 2012107
4 201558
5 201447
6 201839
7 201525
8 201518
9 201816
10 201116
11 201811
12
Detection of defective sources with belief function s
20089
13 20098
14 20197
15
A genetic approach for training diverse classifier ensembles
20084
16 20121
17 20221
18 20061
19 20220

About David Gacquer

David Gacquer is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Management Science and Operations Research, having authored 19 papers that have together received 815 indexed citations. Recurring topics across this work include Gene expression and cancer classification (3 papers), Thyroid Cancer Diagnosis and Treatment (3 papers), RNA Research and Splicing (3 papers), Multi-Criteria Decision Making (2 papers), Evolutionary Algorithms and Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Metaheuristic Optimization Algorithms Research (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Developmental Neuroscience (83 citations), Cancer Research (145 citations), Molecular Biology (552 citations), Aging (12 citations) and Endocrinology, Diabetes and Metabolism (93 citations). David Gacquer has collaborated with scholars based in Belgium, France and United States. Frequent co-authors include Vincent Detours, Julian Chéron, Angéline Bilheu, Pierre Vanderhaeghen, Franck Polleux, Roxane Van Heurck, Adèle Herpoel, Marta Wojno, Nelle Lambert and Ikuo Suzuki. Their work appears in journals such as The Journal of Clinical Endocrinology & Metabolism, BMC Genomics, Cancer Research, Cell Reports and Oncogene.

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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