Michaela Spitzer
- Health Informatics top 1%
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 4
- Molecular Biology top 5%
- Bioinformatics and Genomic Networks 3
- Single-cell and spatial transcriptomics 1
- Infectious Diseases top 5%
- Antifungal resistance and susceptibility 3
- Biophysics top 5%
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- Microbial Natural Products and Biosynthesis 4
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- Fungal Infections and Studies 4
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- Infectious Diseases and Mycology 2
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- Genetic Associations and Epidemiology 1
- Co-authors
- Ian DunhamPaul CzodrowskiJessica VamathevanShanrong ZhaoEdgardo A. FerránParantu K. ShahAnant MadabhushiGeorge Lee
- Partner nations
- United KingdomCanadaUnited States
In The Last Decade
Michaela Spitzer
17 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Health Informatics 93
- Computational Theory and Mathematics 962
- Molecular Biology 1.4k
- Infectious Diseases 350
- Biophysics 99
Countries citing papers authored by Michaela Spitzer
This map shows the geographic impact of Michaela Spitzer'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 Michaela Spitzer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michaela Spitzer more than expected).
Fields of papers citing papers by Michaela Spitzer
This network shows the impact of papers produced by Michaela Spitzer. 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 Michaela Spitzer. The network helps show where Michaela Spitzer may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Michaela Spitzer, 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 | 2022 | 27 | |
| 2 | 2021 | 3 | |
| 3 | 2019 | 28 | |
| 4 | Applications of machine learning in drug discovery and developmentbreakdown → | 2019 | 1696 |
| 5 | Open Targets Platform: new developments and updates two years onbreakdown → | 2018 | 284 |
| 6 | 2016 | 30 | |
| 7 | 2016 | 12 | |
| 8 | 2016 | 155 | |
| 9 | 2015 | 61 | |
| 10 | 2015 | 85 | |
| 11 | 2014 | 3 | |
| 12 | 2013 | 18 | |
| 13 | 2013 | 149 | |
| 14 | 2012 | 40 | |
| 15 | 2011 | 153 | |
| 16 | 2010 | 36 | |
| 17 | 1985 | 1 |
About Michaela Spitzer
Michaela Spitzer is a scholar working on Pharmacology, Small Animals, Computational Theory and Mathematics, Physiology and Biophysics, having authored 17 papers that have together received 2.8k indexed citations. Recurring topics across this work include Microbial Natural Products and Biosynthesis (4 papers), Computational Drug Discovery Methods (4 papers), Fungal Infections and Studies (4 papers), Antifungal resistance and susceptibility (3 papers), Bioinformatics and Genomic Networks (3 papers), Infectious Diseases and Mycology (2 papers), Genetic Associations and Epidemiology (1 paper) and Single-cell and spatial transcriptomics (1 paper). The work is most often cited by research in Health Informatics (93 citations), Computational Theory and Mathematics (962 citations), Molecular Biology (1.4k citations), Infectious Diseases (350 citations) and Biophysics (99 citations). Michaela Spitzer has collaborated with scholars based in United Kingdom, Canada and United States. Frequent co-authors include Ian Dunham, Paul Czodrowski, Jessica Vamathevan, Shanrong Zhao, Edgardo A. Ferrán, Parantu K. Shah, Anant Madabhushi, George Lee, Dominic A. Clark and Bin Li. Their work appears in journals such as Virulence, Cell Genomics, Disease Models & Mechanisms, Cell Reports and Nature Reviews Drug Discovery.
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.