Jan Schröder
Impact in
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies
- Pluripotent Stem Cells Research
- CRISPR and Genetic Engineering
- RNA and protein synthesis mechanisms
- Renal and related cancers
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- Cancer Genomics and Diagnostics
Papers in
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- Genomics and Phylogenetic Studies 11
- Pluripotent Stem Cells Research 3
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- Microtubule and mitosis dynamics 2
- Co-authors
- Bertil Schmidt (4 shared papers)Yongchao Liu (2 shared papers)Leena Salmela (1 shared paper)Anthony T. Papenfuss (10 shared papers)Hongdo Do (1 shared paper)Ramyar Molania (1 shared paper)Daniel Cameron (1 shared paper)Jocelyn Sietsma Penington (1 shared paper)
- Journals
- Bioinformatics (6 papers)PLoS ONE (4 papers)Scientific Reports (2 papers)BMC Bioinformatics (2 papers)iScience (1 paper)
- Partner nations
- AustraliaGermanyUnited States
In The Last Decade
Jan Schröder
34 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Molecular Biology 893
- Cancer Research 134
- Genetics 201
- Biophysics 39
- Structural Biology 8
Countries citing papers authored by Jan Schröder
This map shows the geographic impact of Jan Schröder'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 Schröder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Schröder more than expected).
Fields of papers citing papers by Jan Schröder
This network shows the impact of papers produced by Jan Schröder. 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 Schröder. The network helps show where Jan Schröder may publish in the future.
Co-authors
The 25 scholars most cited alongside Jan Schröder, 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 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Modelling human blastocysts by reprogramming fibroblasts into iBlastoids Hit paper breakdown → | 2021 | 240 |
| 2 | 2012 | 199 | |
| 3 | 2017 | 182 | |
| 4 | 2011 | 110 | |
| 5 | 2009 | 105 | |
| 6 | 2014 | 47 | |
| 7 | 2023 | 42 | |
| 8 | 2021 | 38 | |
| 9 | 2009 | 38 | |
| 10 | 2018 | 33 | |
| 11 | 2010 | 32 | |
| 12 | 2018 | 29 | |
| 13 | 2001 | 28 | |
| 14 | 2014 | 22 | |
| 15 | 2017 | 21 | |
| 16 | 2020 | 19 | |
| 17 | 2010 | 16 | |
| 18 | 2019 | 15 | |
| 19 | 2022 | 13 | |
| 20 | 2015 | 13 |
About Jan Schröder
Jan Schröder is a scholar working on Molecular Biology, Cell Biology, Plant Science, Genetics and Oncology, having authored 37 papers that have together received 1.3k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (11 papers), Genomic variations and chromosomal abnormalities (4 papers), Chromosomal and Genetic Variations (3 papers), Pluripotent Stem Cells Research (3 papers), Algorithms and Data Compression (2 papers), Plant Molecular Biology Research (2 papers), Microtubule and mitosis dynamics (2 papers) and Cancer Genomics and Diagnostics (2 papers). The work is most often cited by research in Molecular Biology (893 citations), Cancer Research (134 citations), Genetics (201 citations), Biophysics (39 citations) and Structural Biology (8 citations). Jan Schröder has collaborated with scholars based in Australia, Germany and United States. Frequent co-authors include Bertil Schmidt, Yongchao Liu, Leena Salmela, Anthony T. Papenfuss, Hongdo Do, Ramyar Molania, Daniel Cameron, Jocelyn Sietsma Penington, Alexander Dobrovic and Terence P. Speed. Their work appears in journals such as Bioinformatics, PLoS ONE, Scientific Reports, BMC Bioinformatics and iScience.
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.