Matej Ušaj
Impact in
- Biophysics top 10%
- Cell Image Analysis Techniques
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- Fungal and yeast genetics research
- CRISPR and Genetic Engineering
- RNA and protein synthesis mechanisms
- Bioinformatics and Genomic Networks
- RNA Research and Splicing
- Genomics and Chromatin Dynamics
Papers in
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- Bioinformatics and Genomic Networks 6
- Fungal and yeast genetics research 4
- Single-cell and spatial transcriptomics 3
- Microbial Metabolic Engineering and Bioproduction 2
- RNA Research and Splicing 2
- CRISPR and Genetic Engineering 2
- RNA and protein synthesis mechanisms 1
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- Cell Image Analysis Techniques 3
- Co-authors
- Brenda Andrews (9 shared papers)Charles Boone (9 shared papers)Chad L. Myers (5 shared papers)Michael Costanzo (4 shared papers)Benjamin VanderSluis (4 shared papers)Elena Kuzmin (2 shared papers)Wen Wang (3 shared papers)Anastasia Baryshnikova (2 shared papers)
- Journals
- Molecular Systems Biology (3 papers)Science (2 papers)G3 Genes Genomes Genetics (2 papers)Nature Structural & Molecular Biology (1 paper)Cell Reports (1 paper)
- Partner nations
- CanadaUnited StatesSpain
In The Last Decade
Matej Ušaj
11 papers receiving 540 citations
Peers
Comparison fields: 5 of 70
- Biophysics 41
- Molecular Biology 483
- Aging 11
- Genetics 91
- Cell Biology 51
Countries citing papers authored by Matej Ušaj
This map shows the geographic impact of Matej Ušaj'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 Matej Ušaj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matej Ušaj more than expected).
Fields of papers citing papers by Matej Ušaj
This network shows the impact of papers produced by Matej Ušaj. 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 Matej Ušaj. The network helps show where Matej Ušaj may publish in the future.
Co-authors
The 25 scholars most cited alongside Matej Ušaj, 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 | 2013 | 110 | |
| 2 | 2017 | 84 | |
| 3 | 2019 | 80 | |
| 4 | 2020 | 70 | |
| 5 | 2022 | 43 | |
| 6 | 2021 | 40 | |
| 7 | 2020 | 38 | |
| 8 | 2020 | 37 | |
| 9 | 2021 | 35 | |
| 10 | 2020 | 6 | |
| 11 | 2024 | 1 |
About Matej Ušaj
Matej Ušaj is a scholar working on Molecular Biology, Biophysics, Cellular and Molecular Neuroscience, Genetics and Biotechnology, having authored 11 papers that have together received 544 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (6 papers), Fungal and yeast genetics research (4 papers), Single-cell and spatial transcriptomics (3 papers), Cell Image Analysis Techniques (3 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), RNA Research and Splicing (2 papers), CRISPR and Genetic Engineering (2 papers) and RNA and protein synthesis mechanisms (1 paper). The work is most often cited by research in Biophysics (41 citations), Molecular Biology (483 citations), Aging (11 citations), Genetics (91 citations) and Cell Biology (51 citations). Matej Ušaj has collaborated with scholars based in Canada, United States and Spain. Frequent co-authors include Brenda Andrews, Charles Boone, Chad L. Myers, Michael Costanzo, Benjamin VanderSluis, Elena Kuzmin, Wen Wang, Anastasia Baryshnikova, Leopold Parts and Omar Wagih. Their work appears in journals such as Molecular Systems Biology, Science, G3 Genes Genomes Genetics, Nature Structural & Molecular Biology and Cell 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.