Daniel Marbach
- Molecular Biology top 2%
- Bioinformatics and Genomic Networks 18
- Gene Regulatory Network Analysis 14
- Gene expression and cancer classification 6
- Genetics, Bioinformatics, and Biomedical Research 4
- Biophysics top 5%
- Cell Image Analysis Techniques 3
- Genetics top 5%
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- Computational Drug Discovery Methods 3
- Aging top 10%
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- Complex Network Analysis Techniques 3
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- Modular Robots and Swarm Intelligence 2
- Co-authors
- Dario FloreanoThomas SchaffterRobert J. PrillGustavo StolovitzkyManolis KellisClaudio MattiussiDiogo M. CamachoJames J. Collins
- Cited by
- Molecular BiologyBiophysicsGenetics
- Partner nations
- SwitzerlandUnited StatesGermany
In The Last Decade
Daniel Marbach
28 papers receiving 3.5k citations
Hit Papers
Peers
Comparison fields: 5 of 151
- Molecular Biology 2.9k
- Biophysics 112
- Genetics 389
- Computational Theory and Mathematics 218
- Aging 21
Countries citing papers authored by Daniel Marbach
This map shows the geographic impact of Daniel Marbach'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 Daniel Marbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Marbach more than expected).
Fields of papers citing papers by Daniel Marbach
This network shows the impact of papers produced by Daniel Marbach. 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 Daniel Marbach. The network helps show where Daniel Marbach may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel Marbach, 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 | 2025 | 2 | |
| 2 | 2024 | 25 | |
| 3 | 2022 | 8 | |
| 4 | 2020 | 12 | |
| 5 | 2020 | 10 | |
| 6 | 2018 | 3 | |
| 7 | 2017 | 17 | |
| 8 | 2016 | 180 | |
| 9 | 2016 | 192 | |
| 10 | 2013 | 183 | |
| 11 | Wisdom of crowds for robust gene network inferencebreakdown → | 2012 | 1110 |
| 12 | 2012 | 89 | |
| 13 | 2012 | 5 | |
| 14 | 2011 | 360 | |
| 15 | Information-Theoretic Inference of Gene Networks Using Backward Elimination | 2010 | 34 |
| 16 | 2009 | 25 | |
| 17 | 2009 | 23 | |
| 18 | 2009 | 301 | |
| 19 | 2008 | 12 | |
| 20 | Co-evolution of Configuration and Control for Homogenous Modular Robots | 2004 | 29 |
About Daniel Marbach
Daniel Marbach is a scholar working on Biophysics, Molecular Biology and Statistical and Nonlinear Physics, having authored 29 papers that have together received 3.6k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (18 papers), Gene Regulatory Network Analysis (14 papers), Gene expression and cancer classification (6 papers), Genetics, Bioinformatics, and Biomedical Research (4 papers), Computational Drug Discovery Methods (3 papers), Complex Network Analysis Techniques (3 papers), Cell Image Analysis Techniques (3 papers) and Modular Robots and Swarm Intelligence (2 papers). The work is most often cited by research in Molecular Biology (2.9k citations), Biophysics (112 citations) and Genetics (389 citations). Daniel Marbach has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Dario Floreano, Thomas Schaffter, Robert J. Prill, Gustavo Stolovitzky, Manolis Kellis, Claudio Mattiussi, Diogo M. Camacho, James J. Collins, Nic M. Vega and Kyle R. Allison. Their work appears in journals such as Nature Methods, Bioinformatics, PLoS Computational Biology, Annals of the New York Academy of Sciences 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.