C. Barnes

26.9k total citations · 4 hit papers
84 papers, 5.0k citations indexed

About

C. Barnes is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, C. Barnes has authored 84 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Molecular Biology, 22 papers in Genetics and 16 papers in Cancer Research. Recurrent topics in C. Barnes's work include Gene Regulatory Network Analysis (22 papers), Cancer Genomics and Diagnostics (14 papers) and Evolution and Genetic Dynamics (12 papers). C. Barnes is often cited by papers focused on Gene Regulatory Network Analysis (22 papers), Cancer Genomics and Diagnostics (14 papers) and Evolution and Genetic Dynamics (12 papers). C. Barnes collaborates with scholars based in United Kingdom, United States and Canada. C. Barnes's co-authors include Michael P. H. Stumpf, Trevor A. Graham, Alex J. H. Fedorec, Andrea Sottoriva, Marc Williams, Benjamin Werner, M. Halpern, M. Limon, S. S. Meyer and E. L. Wright and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

C. Barnes

81 papers receiving 4.9k citations

Hit Papers

First‐Year Wilkinson Micr... 2003 2026 2010 2018 2003 2003 2009 2016 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
C. Barnes 1.9k 1.5k 942 774 614 84 5.0k
Joseph Lehár 2.0k 1.1× 1.4k 1.0× 293 0.3× 166 0.2× 226 0.4× 66 5.6k
Yuval Kluger 7.2k 3.9× 479 0.3× 774 0.8× 706 0.9× 1.6k 2.7× 155 12.4k
Yutaka Komiyama 1.5k 0.8× 4.1k 2.8× 579 0.6× 266 0.3× 293 0.5× 258 10.4k
Иван Тодоров 2.9k 1.6× 341 0.2× 1.0k 1.1× 821 1.1× 738 1.2× 228 7.8k
Takashi Isobe 1.1k 0.6× 553 0.4× 215 0.2× 182 0.2× 92 0.1× 232 4.2k
Vésteinn Thórsson 3.3k 1.7× 214 0.1× 360 0.4× 389 0.5× 285 0.5× 44 4.9k
Allan Jacobson 11.5k 6.2× 517 0.4× 483 0.5× 791 1.0× 462 0.8× 170 14.3k
Carsten Peterson 4.4k 2.4× 122 0.1× 2.2k 2.4× 743 1.0× 713 1.2× 157 10.1k
Paolo Provero 6.2k 3.3× 379 0.3× 635 0.7× 507 0.7× 3.7k 5.9× 157 9.3k
Joel Rozowsky 5.8k 3.1× 400 0.3× 856 0.9× 1.1k 1.4× 2.1k 3.5× 79 7.7k

Countries citing papers authored by C. Barnes

Since Specialization
Citations

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

Fields of papers citing papers by C. Barnes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C. Barnes

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

All Works

20 of 20 papers shown
1.
Duran‐Ferrer, Martí, Diego Mallo, Ferran Nadeu, et al.. (2025). Fluctuating DNA methylation tracks cancer evolution at clinical scale. Nature. 645(8081). 764–773. 2 indexed citations
2.
Ennis, Darren, Bingxin Lu, Hasan Mirza, et al.. (2024). The genomic trajectory of ovarian high‐grade serous carcinoma can be observed in STIC lesions. The Journal of Pathology. 264(1). 42–54. 6 indexed citations
3.
Lu, Bingxin, Kit Curtius, Trevor A. Graham, Ziheng Yang, & C. Barnes. (2023). CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples. Genome biology. 24(1). 144–144. 4 indexed citations
4.
Otero‐Muras, Irene, Rubén Perez‐Carrasco, Julio R. Banga, & C. Barnes. (2023). Automated design of gene circuits with optimal mushroom-bifurcation behavior. iScience. 26(6). 106836–106836. 5 indexed citations
5.
Schenck, Ryan O., Daniel J. Weisenberger, Christopher Kimberley, et al.. (2022). Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nature Biotechnology. 40(5). 720–730. 27 indexed citations
6.
Ingalls, Brian, et al.. (2022). Deep reinforcement learning for optimal experimental design in biology. PLoS Computational Biology. 18(11). e1010695–e1010695. 15 indexed citations
7.
Dekker, Linda, et al.. (2021). Engineered acetoacetate‐inducible whole‐cell biosensors based on the AtoSC two‐component system. Biotechnology and Bioengineering. 118(11). 4278–4289. 11 indexed citations
8.
Fedorec, Alex J. H., et al.. (2020). FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data. ACS Synthetic Biology. 9(9). 2258–2266. 18 indexed citations
9.
Werner, Benjamin, Marc Williams, Daniel Temko, et al.. (2020). Measuring single cell divisions in human tissues from multi-region sequencing data. Nature Communications. 11(1). 1035–1035. 33 indexed citations
10.
Fedorec, Alex J. H., et al.. (2020). Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Computational Biology. 16(4). e1007783–e1007783. 82 indexed citations
11.
Lakatos, Eszter, Marc Williams, Ryan O. Schenck, et al.. (2020). Evolutionary dynamics of neoantigens in growing tumors. Nature Genetics. 52(10). 1057–1066. 60 indexed citations
12.
Caravagna, Giulio, Timon Heide, Marc Williams, et al.. (2020). Subclonal reconstruction of tumors by using machine learning and population genetics. Nature Genetics. 52(9). 898–907. 57 indexed citations
13.
Shaw, Liam P., Bassam Abdul Rasool Hassan, C. Barnes, et al.. (2019). Modelling microbiome recovery after antibiotics using a stability landscape framework. The ISME Journal. 13(7). 1845–1856. 85 indexed citations
14.
Galimov, Evgeniy R., et al.. (2019). Detecting Changes in the Caenorhabditis elegans Intestinal Environment Using an Engineered Bacterial Biosensor. ACS Synthetic Biology. 8(12). 2620–2628. 16 indexed citations
15.
Silk, Daniel, Paul Kirk, C. Barnes, Tina Toni, & Michael P. H. Stumpf. (2014). Model Selection in Systems Biology Depends on Experimental Design. PLoS Computational Biology. 10(6). e1003650–e1003650. 41 indexed citations
16.
Filippi, Sarah, C. Barnes, Julien Cornebise, & Michael P. H. Stumpf. (2013). On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo. Statistical Applications in Genetics and Molecular Biology. 12(1). 87–107. 70 indexed citations
17.
Filippi, Sarah, C. Barnes, & Michael P. H. Stumpf. (2011). On optimal kernel in ABC SMC. arXiv (Cornell University). 2 indexed citations
18.
Surakka, Ida, Kati Kristiansson, Verneri Anttila, et al.. (2010). Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging. Genome Research. 20(10). 1344–1351. 36 indexed citations
19.
McGuinness, Deborah L., et al.. (2006). Towards a Reference Volcano Ontology for Semantic Scientific Data Integration. AGUSM. 2007. 3 indexed citations
20.
Toumi, Ralf, et al.. (2001). Robust non‐Gaussian statistics and long‐range correlation of total ozone. Atmospheric Science Letters. 2(1-4). 94–103. 5 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026