Uğis Sarkans
- Biophysics top 1%
- Cell Image Analysis Techniques 4
- Molecular Biology top 5%
- Gene expression and cancer classification 17
- Bioinformatics and Genomic Networks 12
- Biomedical Text Mining and Ontologies 8
- Genomics and Phylogenetic Studies 6
- Genetics, Bioinformatics, and Biomedical Research 3
- Single-cell and spatial transcriptomics 3
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- Scientific Computing and Data Management 6
- Cancer Research top 10%
- Structural Biology top 10%
- Co-authors
- Alvis BrāzmaHelen ParkinsonRobert PetryszakMisha KapusheskyAwais AtharAhmed Yousif AliGabriella RusticiCatherine Snow
- Partner nations
- United KingdomGermanyFinland
In The Last Decade
Uğis Sarkans
29 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 142
- Biophysics 262
- Molecular Biology 1.9k
- Information Systems and Management 132
- Cancer Research 289
- Structural Biology 24
Countries citing papers authored by Uğis Sarkans
This map shows the geographic impact of Uğis Sarkans'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 Uğis Sarkans with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Uğis Sarkans more than expected).
Fields of papers citing papers by Uğis Sarkans
This network shows the impact of papers produced by Uğis Sarkans. 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 Uğis Sarkans. The network helps show where Uğis Sarkans may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Uğis Sarkans, 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 | 2 | |
| 2 | 2023 | 2 | |
| 3 | 2022 | 64 | |
| 4 | 2021 | 6 | |
| 5 | ArrayExpress update – from bulk to single-cell expression databreakdown → | 2018 | 377 |
| 6 | 2017 | 160 | |
| 7 | 2017 | 6 | |
| 8 | 2017 | 129 | |
| 9 | 2015 | 21 | |
| 10 | ArrayExpress update—simplifying data submissionsbreakdown → | 2014 | 509 |
| 11 | 2013 | 19 | |
| 12 | 2013 | 142 | |
| 13 | 2011 | 42 | |
| 14 | 2009 | 14 | |
| 15 | 2006 | 101 | |
| 16 | ArrayExpress--a public database of microarray experiments and gene expression profilesbreakdown → | 2006 | 539 |
| 17 | 2004 | 94 | |
| 18 | 2004 | 22 | |
| 19 | 2003 | 67 | |
| 20 | 2002 | 23 |
About Uğis Sarkans
Uğis Sarkans is a scholar working on Structural Biology, Information Systems and Management and Biophysics, having authored 29 papers that have together received 2.5k indexed citations. Recurring topics across this work include Gene expression and cancer classification (17 papers), Bioinformatics and Genomic Networks (12 papers), Biomedical Text Mining and Ontologies (8 papers), Genomics and Phylogenetic Studies (6 papers), Scientific Computing and Data Management (6 papers), Cell Image Analysis Techniques (4 papers), Genetics, Bioinformatics, and Biomedical Research (3 papers) and Single-cell and spatial transcriptomics (3 papers). The work is most often cited by research in Biophysics (262 citations), Molecular Biology (1.9k citations) and Information Systems and Management (132 citations). Uğis Sarkans has collaborated with scholars based in United Kingdom, Germany and Finland. Frequent co-authors include Alvis Brāzma, Helen Parkinson, Robert Petryszak, Misha Kapushesky, Awais Athar, Ahmed Yousif Ali, Gabriella Rustici, Catherine Snow, Eleanor Williams and Niran Abeygunawardena. Their work appears in journals such as Nucleic Acids Research, Bioinformatics, Nature Methods, PLANT PHYSIOLOGY and Advances in biochemical engineering, biotechnology.
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.