Scott C. Schmidler
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods 7
- Statistical Methods and Inference 6
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- Protein Structure and Dynamics 11
- Genomics and Phylogenetic Studies 5
- RNA and protein synthesis mechanisms 5
- Artificial Intelligence top 10%
- Bayesian Methods and Mixture Models 8
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- Genetic diversity and population structure 3
- Ophthalmology top 10%
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- Enzyme Structure and Function 5
- Co-authors
- Douglas L. BrutlagJun S. LiuDawn B. WoodardMark HuberChunlin JiKatia KoelleRotem Ben‐ShacharThomas D. Wu
- Journals
- Journal of the American Chemical Society (2 papers)The Journal of Chemical Physics (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Scott C. Schmidler
32 papers receiving 699 citations
Peers
Comparison fields: 5 of 129
- Statistics and Probability 122
- Molecular Biology 342
- Artificial Intelligence 112
- Genetics 97
- Ophthalmology 28
Countries citing papers authored by Scott C. Schmidler
This map shows the geographic impact of Scott C. Schmidler'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 C. Schmidler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott C. Schmidler more than expected).
Fields of papers citing papers by Scott C. Schmidler
This network shows the impact of papers produced by Scott C. Schmidler. 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 C. Schmidler. The network helps show where Scott C. Schmidler may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Scott C. Schmidler, 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 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2020 | 8 | |
| 4 | 2020 | 12 | |
| 5 | 2017 | 6 | |
| 6 | 2017 | 9 | |
| 7 | 2016 | 40 | |
| 8 | 2014 | 26 | |
| 9 | 2014 | 40 | |
| 10 | 2012 | 21 | |
| 11 | 2010 | 52 | |
| 12 | 2010 | 8 | |
| 13 | 2008 | 27 | |
| 14 | 2008 | 22 | |
| 15 | 2008 | 28 | |
| 16 | 2008 | 49 | |
| 17 | 2007 | 0 | |
| 18 | 1998 | 16 | |
| 19 | Modeling and superposition of multiple protein structures using affine transformations: analysis of the globins. | 1998 | 6 |
| 20 | The COLLAGE/KHOROS Link: Planning for Image Processing Tasks | 1995 | 15 |
About Scott C. Schmidler
Scott C. Schmidler is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology, having authored 33 papers that have together received 727 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (11 papers), Bayesian Methods and Mixture Models (8 papers), Markov Chains and Monte Carlo Methods (7 papers), Statistical Methods and Inference (6 papers), Genomics and Phylogenetic Studies (5 papers), RNA and protein synthesis mechanisms (5 papers), Enzyme Structure and Function (5 papers) and Genetic diversity and population structure (3 papers). The work is most often cited by research in Statistics and Probability (122 citations), Molecular Biology (342 citations) and Artificial Intelligence (112 citations). Scott C. Schmidler has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Douglas L. Brutlag, Jun S. Liu, Dawn B. Woodard, Mark Huber, Chunlin Ji, Katia Koelle, Rotem Ben‐Shachar, Thomas D. Wu, Trevor Hastie and Rolando Estrada. Their work appears in journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and PLoS ONE.
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.