Gábor Balázsi

6.7k total citations · 1 hit paper
84 papers, 4.1k citations indexed

About

Gábor Balázsi is a scholar working on Molecular Biology, Genetics and Ecology. According to data from OpenAlex, Gábor Balázsi has authored 84 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Molecular Biology, 29 papers in Genetics and 7 papers in Ecology. Recurrent topics in Gábor Balázsi's work include Gene Regulatory Network Analysis (49 papers), Bioinformatics and Genomic Networks (17 papers) and Bacterial Genetics and Biotechnology (17 papers). Gábor Balázsi is often cited by papers focused on Gene Regulatory Network Analysis (49 papers), Bioinformatics and Genomic Networks (17 papers) and Bacterial Genetics and Biotechnology (17 papers). Gábor Balázsi collaborates with scholars based in United States, Russia and Canada. Gábor Balázsi's co-authors include James J. Collins, Alexander van Oudenaarden, Kevin Murphy, Dmitry Nevozhay, Rhys Adams, Zoltán N. Oltvai, Daniel A. Charlebois, Diogo F. T. Veiga, Maria Laura Gennaro and Yina Kuang and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Gábor Balázsi

84 papers receiving 4.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Cellular Decision Making and Biological Noise: From Microbes to Mammals

2011 723 citations Gábor Balázsi, James J. Collins et al. profile →

Peers

Gábor Balázsi

MB

GENET

ID

EPIDE

BIOPH

Peter Uetz United States

Philip M. Kim Canada

Jack Merrin Austria

Michael Springer United States

Gene‐Wei Li United States

Yu Xia United States

Leopold Parts United Kingdom

Avigdor Eldar Israel

Hana El‐Samad United States

Timothy Galitski United States

Peter Uetz United States

Gábor Balázsi

7.2k ×2.1

MB

838 ×0.8

GENET

197 ×0.6

ID

353 ×1.1

EPIDE

71 ×0.3

BIOPH

Citations per year, relative to Gábor Balázsi Gábor Balázsi (= 1×) peers Peter Uetz

Countries citing papers authored by Gábor Balázsi

Since Specialization

Citations

This map shows the geographic impact of Gábor Balázsi'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 Gábor Balázsi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gábor Balázsi more than expected).

Fields of papers citing papers by Gábor Balázsi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gábor Balázsi. 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 Gábor Balázsi. The network helps show where Gábor Balázsi may publish in the future.

Co-authorship network of co-authors of Gábor Balázsi

This figure shows the co-authorship network connecting the top 25 collaborators of Gábor Balázsi. A scholar is included among the top collaborators of Gábor Balázsi 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 Gábor Balázsi. Gábor Balázsi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Dill, Ken A., et al.. (2024). Transcriptional drift in aging cells: A global decontroller. Proceedings of the National Academy of Sciences. 121(30). e2401830121–e2401830121. 1 indexed citations

2.

Balázsi, Gábor, et al.. (2024). Synthetic gene circuit evolution: Insights and opportunities at the mid-scale. Cell chemical biology. 31(8). 1447–1459. 4 indexed citations

3.

Szenk, Mariola, et al.. (2023). Immune mechanisms shape the clonal landscape during early progression of prostate cancer. Developmental Cell. 58(12). 1071–1086.e8. 4 indexed citations

4.

Balázsi, Gábor, et al.. (2022). Drug-dependent growth curve reshaping reveals mechanisms of antifungal resistance in Saccharomyces cerevisiae. Communications Biology. 5(1). 292–292. 3 indexed citations

5.

Charlebois, Daniel A., et al.. (2019). Role of network-mediated stochasticity in mammalian drug resistance. Nature Communications. 10(1). 2766–2766. 67 indexed citations

6.

Charlebois, Daniel A., et al.. (2018). Multiscale effects of heating and cooling on genes and gene networks. Proceedings of the National Academy of Sciences. 115(45). E10797–E10806. 41 indexed citations

7.

Charlebois, Daniel A. & Gábor Balázsi. (2018). Modeling cell population dynamics. In Silico Biology. 13(1-2). 21–39. 46 indexed citations

8.

Székely, Tamás, Elizabeth Diago‐Navarro, Erika P. Orner, et al.. (2017). Generational distribution of a Candida glabrata population: Resilient old cells prevail, while younger cells dominate in the vulnerable host. PLoS Pathogens. 13(5). e1006355–e1006355. 24 indexed citations

9.

Zeng, Lanying, et al.. (2017). Late-Arriving Signals Contribute Less to Cell-Fate Decisions. Biophysical Journal. 113(9). 2110–2120. 18 indexed citations

10.

Székely, Tamás, et al.. (2017). Cell fate decisions emerge as phages cooperate or compete inside their host. Nature Communications. 8(1). 14341–14341. 55 indexed citations

11.

Balázsi, Gábor, et al.. (2015). Optimality and adaptation of phenotypically switching cells in fluctuating environments. Physical Review E. 92(6). 62716–62716. 15 indexed citations

12.

Charlebois, Daniel A., Gábor Balázsi, & Mads Kærn. (2014). Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Physical Review E. 89(5). 52708–52708. 30 indexed citations

13.

Chen, Lin, Javad Noorbakhsh, Rhys Adams, et al.. (2014). Two-Dimensionality of Yeast Colony Expansion Accompanied by Pattern Formation. PLoS Computational Biology. 10(12). e1003979–e1003979. 32 indexed citations

14.

Nevozhay, Dmitry, Rhys Adams, Elizabeth Van Itallie, Matthew R. Bennett, & Gábor Balázsi. (2012). Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit. PLoS Computational Biology. 8(4). e1002480–e1002480. 102 indexed citations

15.

Murphy, Kevin, Rhys Adams, Xiao Wang, Gábor Balázsi, & James J. Collins. (2010). Tuning and controlling gene expression noise in synthetic gene networks. Nucleic Acids Research. 38(8). 2712–2726. 110 indexed citations

16.

Veiga, Diogo F. T., Bhaskar Dutta, & Gábor Balázsi. (2009). Network inference and network response identification: moving genome-scale data to the next level of biological discovery. Molecular BioSystems. 6(3). 469–480. 33 indexed citations

17.

Balázsi, Gábor & Zoltán N. Oltvai. (2007). A Pitfall in Series of Microarrays. Methods in molecular biology. 377. 153–161. 7 indexed citations

18.

Blake, William J., Gábor Balázsi, Michael A. Kohanski, et al.. (2006). Phenotypic Consequences of Promoter-Mediated Transcriptional Noise. Molecular Cell. 24(6). 853–865. 465 indexed citations

19.

Balázsi, Gábor, Albert-Ĺaszló Barabási, & Zoltán N. Oltvai. (2005). Topological units of environmental signal processing in the transcriptional-regulatory network of Escherichia coli. Bulletin of the American Physical Society. 8 indexed citations

20.

Balázsi, Gábor. (2003). Spurious spatial periodicity of co-expression in microarray data due to printing design. Nucleic Acids Research. 31(15). 4425–4433. 31 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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