Jan Aerts
- Immunology top 2%
- Cancer Research top 2%
- Molecular Biology top 2%
- Genomics and Phylogenetic Studies 15
- Bioinformatics and Genomic Networks 13
- Gene expression and cancer classification 9
- Biomedical Text Mining and Ontologies 5
- Gene Regulatory Network Analysis 5
- Genetics top 2%
- Genomics and Rare Diseases 7
- Biophysics top 2%
- Cell Image Analysis Techniques 7
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- Data Visualization and Analytics 10
- Co-authors
- Sara AibarStein AertsCarmen Bravo González‐BlasThomas MoermanFlorian RambowZeynep Kalender AtakGert HulselmansJean‐Christophe Marine
- Partner nations
- BelgiumUnited KingdomUnited States
In The Last Decade
Jan Aerts
60 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Immunology 1.1k
- Cancer Research 736
- Molecular Biology 3.3k
- Genetics 893
- Biophysics 166
Countries citing papers authored by Jan Aerts
This map shows the geographic impact of Jan Aerts'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 Jan Aerts with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Aerts more than expected).
Fields of papers citing papers by Jan Aerts
This network shows the impact of papers produced by Jan Aerts. 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 Jan Aerts. The network helps show where Jan Aerts may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jan Aerts, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2023 | 0 | |
| 3 | 2018 | 3 | |
| 4 | 2016 | 72 | |
| 5 | 2014 | 6 | |
| 6 | 2013 | 34 | |
| 7 | 2013 | 125 | |
| 8 | 2013 | 7 | |
| 9 | Visualizing high dimensional datasets using parallel coordinaties : application to gene prioritization | 2012 | 1 |
| 10 | Exome sequencing and genetic testing for monogenic diabetes | 2012 | 2 |
| 11 | 2012 | 88 | |
| 12 | 2012 | 2 | |
| 13 | 2012 | 26 | |
| 14 | 2011 | 460 | |
| 15 | 2009 | 36 | |
| 16 | 2007 | 37 | |
| 17 | 2007 | 198 | |
| 18 | 2005 | 5 | |
| 19 | 2003 | 15 | |
| 20 | 2002 | 16 |
About Jan Aerts
Jan Aerts is a scholar working on Biophysics, Genetics, Computer Vision and Pattern Recognition, Molecular Biology and Information Systems and Management, having authored 65 papers that have together received 5.4k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (15 papers), Bioinformatics and Genomic Networks (13 papers), Data Visualization and Analytics (10 papers), Gene expression and cancer classification (9 papers), Genomics and Rare Diseases (7 papers), Cell Image Analysis Techniques (7 papers), Biomedical Text Mining and Ontologies (5 papers) and Gene Regulatory Network Analysis (5 papers). The work is most often cited by research in Immunology (1.1k citations), Cancer Research (736 citations), Molecular Biology (3.3k citations), Genetics (893 citations) and Biophysics (166 citations). Jan Aerts has collaborated with scholars based in Belgium, United Kingdom and United States. Frequent co-authors include Sara Aibar, Stein Aerts, Carmen Bravo González‐Blas, Thomas Moerman, Florian Rambow, Zeynep Kalender Atak, Gert Hulselmans, Jean‐Christophe Marine, Vân Anh Huynh‐Thu and Pierre Geurts. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, PeerJ Computer Science, Animal Genetics and Nucleic Acids Research.
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.