Scott Schwartz
Impact in
- Molecular Biology top 1%
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Genomics and Chromatin Dynamics
- RNA Research and Splicing
- Genetics top 1%
- Genetic diversity and population structure
Papers in
-
- RNA and protein synthesis mechanisms 17
- Genomics and Phylogenetic Studies 15
- Machine Learning in Bioinformatics 5
- Genomics and Chromatin Dynamics 4
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- Statistical Methods and Bayesian Inference 2
- Co-authors
- Webb MillerZheng ZhangLukas WagnerRoss C. HardisonArian F. A. SmitDavid HausslerW. James KentCathy Riemer
- Journals
- Genome Research (8 papers)Genomics (3 papers)Genome biology (2 papers)Nucleic Acids Research (2 papers)Statistics in Medicine (2 papers)
- Partner nations
- United StatesRussiaAustralia
In The Last Decade
Scott Schwartz
35 papers receiving 8.2k citations
Hit Papers
Peers
Comparison fields: 5 of 183
- Molecular Biology 5.0k
- Genetics 1.8k
- Plant Science 2.2k
- Ecology 1.1k
- Endocrinology 181
Countries citing papers authored by Scott Schwartz
This map shows the geographic impact of Scott Schwartz'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 Scott Schwartz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Schwartz more than expected).
Fields of papers citing papers by Scott Schwartz
This network shows the impact of papers produced by Scott Schwartz. 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 Scott Schwartz. The network helps show where Scott Schwartz may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Scott Schwartz, 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 | 2020 | 30 | |
| 2 | 2019 | 15 | |
| 3 | 2017 | 11 | |
| 4 | 2016 | 41 | |
| 5 | 2012 | 82 | |
| 6 | 2010 | 15 | |
| 7 | 2004 | 64 | |
| 8 | 2003 | 183 | |
| 9 | 2003 | 7 | |
| 10 | 2003 | 37 | |
| 11 | 2003 | 107 | |
| 12 | 2003 | 241 | |
| 13 | 2002 | 60 | |
| 14 | Human–Mouse Alignments with BLASTZ Hit paper breakdown → | 2002 | 930 |
| 15 | 2002 | 32 | |
| 16 | A Greedy Algorithm for Aligning DNA Sequences Hit paper breakdown → | 2000 | 4106 |
| 17 | 1994 | 27 | |
| 18 | 1992 | 24 | |
| 19 | 1991 | 29 | |
| 20 | 1991 | 27 |
About Scott Schwartz
Scott Schwartz is a scholar working on Molecular Biology, Statistics and Probability, Plant Science, Agronomy and Crop Science and Developmental Neuroscience, having authored 36 papers that have together received 8.4k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (17 papers), Genomics and Phylogenetic Studies (15 papers), Chromosomal and Genetic Variations (7 papers), Machine Learning in Bioinformatics (5 papers), Genomics and Chromatin Dynamics (4 papers), Bioenergy crop production and management (3 papers), Algorithms and Data Compression (3 papers) and Statistical Methods and Bayesian Inference (2 papers). The work is most often cited by research in Molecular Biology (5.0k citations), Genetics (1.8k citations), Plant Science (2.2k citations), Ecology (1.1k citations) and Endocrinology (181 citations). Scott Schwartz has collaborated with scholars based in United States, Russia and Australia. Frequent co-authors include Webb Miller, Zheng Zhang, Lukas Wagner, Ross C. Hardison, Arian F. A. Smit, David Haussler, W. James Kent, Cathy Riemer, Zheng Zhang and Richard A. Gibbs. Their work appears in journals such as Genome Research, Genomics, Genome biology, Nucleic Acids Research and Statistics in Medicine.
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.