Priit Adler
Impact in
- Cancer Research top 2%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Molecular Biology top 2%
- Bioinformatics and Genomic Networks
- RNA modifications and cancer
- RNA Research and Splicing
- Gene expression and cancer classification
- Epigenetics and DNA Methylation
- Single-cell and spatial transcriptomics
Papers in
-
- Bioinformatics and Genomic Networks 12
- Gene expression and cancer classification 9
- Gene Regulatory Network Analysis 4
- Genomics and Phylogenetic Studies 2
- Machine Learning in Bioinformatics 1
Priit Adler
18 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Cancer Research 939
- Molecular Biology 3.5k
- Aging 83
- Immunology 823
- Genetics 857
Countries citing papers authored by Priit Adler
This map shows the geographic impact of Priit Adler'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 Priit Adler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Priit Adler more than expected).
Fields of papers citing papers by Priit Adler
This network shows the impact of papers produced by Priit Adler. 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 Priit Adler. The network helps show where Priit Adler may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Priit Adler, 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 | g:Profiler—interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update) Hit paper breakdown → | 2023 | 557 |
| 2 | 2023 | 7 | |
| 3 | 2020 | 3 | |
| 4 | 2019 | 34 | |
| 5 | g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) Hit paper breakdown → | 2019 | 3248 |
| 6 | 2018 | 2 | |
| 7 | 2018 | 23 | |
| 8 | 2017 | 188 | |
| 9 | g:Profiler—a web server for functional interpretation of gene lists (2016 update) Hit paper breakdown → | 2016 | 885 |
| 10 | 2015 | 21 | |
| 11 | 2015 | 9 | |
| 12 | 2014 | 30 | |
| 13 | Robust rank aggregation for gene list integration and meta-analysis Hit paper breakdown → | 2012 | 732 |
| 14 | 2010 | 27 | |
| 15 | 2009 | 113 | |
| 16 | 2009 | 11 | |
| 17 | 2008 | 66 | |
| 18 | 2007 | 24 |
About Priit Adler
Priit Adler is a scholar working on Molecular Biology, Biophysics, Physiology, Obstetrics and Gynecology and Cell Biology, having authored 18 papers that have together received 6.0k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (12 papers), Gene expression and cancer classification (9 papers), Gene Regulatory Network Analysis (4 papers), Endoplasmic Reticulum Stress and Disease (2 papers), Genomics and Phylogenetic Studies (2 papers), Pregnancy and preeclampsia studies (1 paper), Machine Learning in Bioinformatics (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Cancer Research (939 citations), Molecular Biology (3.5k citations), Aging (83 citations), Immunology (823 citations) and Genetics (857 citations). Priit Adler has collaborated with scholars based in Estonia, United Kingdom and Finland. Frequent co-authors include Jaak Vilo, Hedi Peterson, Liis Kolberg, T. V. Arak, Ivan Kuzmin, Uku Raudvere, Raivo Kolde, Sven Laur, Jüri Reimand and Sulev Reisberg. Their work appears in journals such as Nucleic Acids Research, Bioinformatics, Genome biology, Molecular Biology of the Cell and Scientific Reports.
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.