Michaela Spitzer

5.8k total citations · 2 hit papers
17 papers, 2.8k citations indexed

About

Michaela Spitzer is a scholar working on Molecular Biology, Pharmacology and Epidemiology. According to data from OpenAlex, Michaela Spitzer has authored 17 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 5 papers in Pharmacology and 5 papers in Epidemiology. Recurrent topics in Michaela Spitzer's work include Microbial Natural Products and Biosynthesis (4 papers), Computational Drug Discovery Methods (4 papers) and Fungal Infections and Studies (4 papers). Michaela Spitzer is often cited by papers focused on Microbial Natural Products and Biosynthesis (4 papers), Computational Drug Discovery Methods (4 papers) and Fungal Infections and Studies (4 papers). Michaela Spitzer collaborates with scholars based in United Kingdom, Canada and United States. Michaela Spitzer's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Molecular and Cellular Biology.

In The Last Decade

Michaela Spitzer

17 papers receiving 2.7k citations

Hit Papers

Applications of machine learning in drug discovery and de... 2018 2026 2020 2023 2019 2018 500 1000 1.5k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michaela Spitzer United Kingdom 13 1.4k 962 447 350 265 17 2.8k
Jinn‐Moon Yang Taiwan 32 2.3k 1.6× 894 0.9× 253 0.6× 358 1.0× 360 1.4× 157 4.3k
María Martin United Kingdom 28 3.1k 2.3× 842 0.9× 433 1.0× 328 0.9× 292 1.1× 81 4.3k
Paul Czodrowski Germany 21 2.5k 1.8× 1.3k 1.3× 738 1.7× 176 0.5× 139 0.5× 41 4.2k
Horacio Pérez‐Sánchez Spain 34 1.8k 1.3× 954 1.0× 409 0.9× 146 0.4× 152 0.6× 219 4.2k
Hua Gao China 27 1.5k 1.1× 1.8k 1.9× 999 2.2× 189 0.5× 161 0.6× 179 4.5k
Mark A. Moraes United States 6 1.8k 1.3× 827 0.9× 297 0.7× 294 0.8× 151 0.6× 7 3.3k
Shawn French Canada 22 1.3k 0.9× 583 0.6× 349 0.8× 210 0.6× 111 0.4× 37 2.6k
Rengül Çetin-Atalay Türkiye 33 2.0k 1.5× 670 0.7× 312 0.7× 95 0.3× 266 1.0× 140 3.8k
Weiwei Xue China 41 3.3k 2.4× 1.0k 1.1× 232 0.5× 740 2.1× 243 0.9× 177 5.7k
Rashmi Kumari India 9 2.1k 1.5× 1.1k 1.2× 196 0.4× 458 1.3× 182 0.7× 40 4.1k

Countries citing papers authored by Michaela Spitzer

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Michaela Spitzer

This figure shows the co-authorship network connecting the top 25 collaborators of Michaela Spitzer. A scholar is included among the top collaborators of Michaela Spitzer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michaela Spitzer. Michaela Spitzer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Bossini‐Castillo, Lara, Dafni A. Glinos, Natalia Kunowska, et al.. (2022). Immune disease variants modulate gene expression in regulatory CD4+ T cells. Cell Genomics. 2(4). 100117–100117. 27 indexed citations
3.
Trávníčková, Jana, Ava Khamseh, Philippe Gautier, et al.. (2019). Zebrafish MITF-Low Melanoma Subtype Models Reveal Transcriptional Subclusters and MITF-Independent Residual Disease. Cancer Research. 79(22). 5769–5784. 28 indexed citations
4.
Vamathevan, Jessica, Dominic A. Clark, Paul Czodrowski, et al.. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery. 18(6). 463–477. 1696 indexed citations breakdown →
5.
Carvalho‐Silva, Denise, Andrea Pierleoni, Miguel Pignatelli, et al.. (2018). Open Targets Platform: new developments and updates two years on. Nucleic Acids Research. 47(D1). D1056–D1065. 284 indexed citations breakdown →
6.
Robbins, Nicole, Michaela Spitzer, Wenliang Wang, et al.. (2016). Discovery of Ibomycin, a Complex Macrolactone that Exerts Antifungal Activity by Impeding Endocytic Trafficking and Membrane Function. Cell chemical biology. 23(11). 1383–1394. 30 indexed citations
7.
Wildenhain, Jan, Michaela Spitzer, Sonam Dolma, et al.. (2016). Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism. Scientific Data. 3(1). 160095–160095. 12 indexed citations
8.
Spitzer, Michaela, Nicole Robbins, & Gerard D. Wright. (2016). Combinatorial strategies for combating invasive fungal infections. Virulence. 8(2). 169–185. 155 indexed citations
9.
Robbins, Nicole, Michaela Spitzer, Yong‐Sun Bahn, et al.. (2015). An Antifungal Combination Matrix Identifies a Rich Pool of Adjuvant Molecules that Enhance Drug Activity against Diverse Fungal Pathogens. Cell Reports. 13(7). 1481–1492. 61 indexed citations
10.
Wildenhain, Jan, Michaela Spitzer, Sonam Dolma, et al.. (2015). Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning. Cell Systems. 1(6). 383–395. 85 indexed citations
11.
White, Sharon A., Alexander Kagansky, Daniel J. St‐Cyr, et al.. (2014). Panspecies Small-Molecule Disruptors of Heterochromatin-Mediated Transcriptional Gene Silencing. Molecular and Cellular Biology. 35(4). 662–674. 3 indexed citations
12.
Wong, Lai Hong, Asier Unciti‐Broceta, Michaela Spitzer, et al.. (2013). A Yeast Chemical Genetic Screen Identifies Inhibitors of Human Telomerase. Chemistry & Biology. 20(3). 333–340. 18 indexed citations
13.
Peil, Lauri, Agata L. Starosta, Jürgen Lassak, et al.. (2013). Distinct XPPX sequence motifs induce ribosome stalling, which is rescued by the translation elongation factor EF-P. Proceedings of the National Academy of Sciences. 110(38). 15265–15270. 149 indexed citations
14.
Zhou, Linna, Hironori Ishizaki, Michaela Spitzer, et al.. (2012). ALDH2 Mediates 5-Nitrofuran Activity in Multiple Species. Chemistry & Biology. 19(7). 883–892. 40 indexed citations
15.
Spitzer, Michaela, Emma Griffiths, Kim M. Blakely, et al.. (2011). Cross‐species discovery of syncretic drug combinations that potentiate the antifungal fluconazole. Molecular Systems Biology. 7(1). 499–499. 153 indexed citations
16.
Ishizaki, Hironori, Michaela Spitzer, Jan Wildenhain, et al.. (2010). Combined zebrafish-yeast chemical-genetic screens reveal gene–copper-nutrition interactions that modulate melanocyte pigmentation. Disease Models & Mechanisms. 3(9-10). 639–651. 36 indexed citations
17.
Moser, R., et al.. (1985). Cure of epoxy resins with esters of cyanoacetic acid. Journal of Polymer Science Polymer Chemistry Edition. 23(9). 2341–2359. 1 indexed citations

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.

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