Jan Aerts
Impact in
- Immunology top 2%
- Immune cells in cancer
- Immune Cell Function and Interaction
- Cancer Research top 2%
- Cancer-related molecular mechanisms research
Papers in
-
- Genomics and Phylogenetic Studies 15
- Bioinformatics and Genomic Networks 13
- Gene expression and cancer classification 9
- Gene Regulatory Network Analysis 5
- Biomedical Text Mining and Ontologies 5
- Genetics 17
- Genomics and Rare Diseases 7
- Co-authors
- Sara Aibar (2 shared papers)Thomas Moerman (2 shared papers)Carmen Bravo González‐Blas (2 shared papers)Stein Aerts (2 shared papers)Joost van den Oord (1 shared paper)Hana Imrichová (1 shared paper)Pierre Geurts (1 shared paper)Gert Hulselmans (1 shared paper)
- Journals
- Bioinformatics (6 papers)BMC Bioinformatics (4 papers)PeerJ Computer Science (3 papers)Animal Genetics (2 papers)Nucleic Acids Research (2 papers)
- Partner nations
- BelgiumUnited KingdomUnited States
In The Last Decade
Jan Aerts
60 papers receiving 5.3k citations
Jan Aerts's 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-authors
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
Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | SCENIC: single-cell regulatory network inference and clustering Hit paper breakdown → | 2017 | 3087 |
| 2 | 2011 | 460 | |
| 3 | 2018 | 292 | |
| 4 | 2007 | 198 | |
| 5 | Encyclopedia of Life Sciences | 2009 | 174 |
| 6 | 2013 | 125 | |
| 7 | 2010 | 116 | |
| 8 | 2008 | 102 | |
| 9 | 2012 | 88 | |
| 10 | 2016 | 72 | |
| 11 | 2019 | 57 | |
| 12 | 2014 | 56 | |
| 13 | 2011 | 40 | |
| 14 | 2007 | 37 | |
| 15 | 2009 | 36 | |
| 16 | 2012 | 36 | |
| 17 | 2013 | 34 | |
| 18 | 2009 | 31 | |
| 19 | 2007 | 31 | |
| 20 | 2012 | 26 |
About Jan Aerts
Jan Aerts is a scholar working on Molecular Biology, Genetics, Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics, 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), Cell Image Analysis Techniques (7 papers), Genomics and Rare Diseases (7 papers), Gene Regulatory Network Analysis (5 papers) and Biomedical Text Mining and Ontologies (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, Thomas Moerman, Carmen Bravo González‐Blas, Stein Aerts, Joost van den Oord, Hana Imrichová, Pierre Geurts, Gert Hulselmans, Jasper Wouters and Vân Anh Huynh‐Thu. 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.