Pablo G. Cámara

3.2k total citations
37 papers, 1.5k citations indexed

About

Pablo G. Cámara is a scholar working on Molecular Biology, Nuclear and High Energy Physics and Astronomy and Astrophysics. According to data from OpenAlex, Pablo G. Cámara has authored 37 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 16 papers in Nuclear and High Energy Physics and 13 papers in Astronomy and Astrophysics. Recurrent topics in Pablo G. Cámara's work include Black Holes and Theoretical Physics (16 papers), Cosmology and Gravitation Theories (13 papers) and Single-cell and spatial transcriptomics (10 papers). Pablo G. Cámara is often cited by papers focused on Black Holes and Theoretical Physics (16 papers), Cosmology and Gravitation Theories (13 papers) and Single-cell and spatial transcriptomics (10 papers). Pablo G. Cámara collaborates with scholars based in United States, Spain and France. Pablo G. Cámara's co-authors include Luis E. Ibáñez, Ángel M. Uranga, Anamarı́a Font, Raúl Rabadán, Gerardo Aldazabal, Kiya W. Govek, Fernando Marchesano, Arnold J. Levine, Hao Wu and Peng Hu and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Nature Methods.

In The Last Decade

Pablo G. Cámara

35 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pablo G. Cámara United States 22 738 605 463 171 161 37 1.5k
Mark R. Dowling Australia 29 202 0.3× 160 0.3× 795 1.7× 265 1.5× 210 1.3× 72 2.7k
María Rodríguez Martínez Switzerland 24 281 0.4× 314 0.5× 1.5k 3.2× 144 0.8× 401 2.5× 66 2.6k
Lindsay King United States 26 236 0.3× 1.2k 1.9× 215 0.5× 59 0.3× 210 1.3× 74 1.8k
Michele Caselle Italy 29 730 1.0× 130 0.2× 1.0k 2.3× 360 2.1× 78 0.5× 160 2.5k
Walter Dittrich Germany 22 653 0.9× 324 0.5× 224 0.5× 160 0.9× 49 0.3× 81 1.5k
Carl Herrmann Germany 24 164 0.2× 88 0.1× 1.5k 3.3× 126 0.7× 258 1.6× 56 2.4k
Markus Schulze Germany 29 1.4k 1.9× 203 0.3× 338 0.7× 22 0.1× 87 0.5× 75 2.4k
Nikodem J. Popławski United States 19 608 0.8× 742 1.2× 179 0.4× 119 0.7× 123 0.8× 37 1.2k
Michael McGuigan United States 12 226 0.3× 205 0.3× 160 0.3× 126 0.7× 29 0.2× 35 796
Dipankar Ray United States 25 153 0.2× 165 0.3× 1000 2.2× 85 0.5× 548 3.4× 102 1.7k

Countries citing papers authored by Pablo G. Cámara

Since Specialization
Citations

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

Fields of papers citing papers by Pablo G. Cámara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pablo G. Cámara. 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 Pablo G. Cámara. The network helps show where Pablo G. Cámara may publish in the future.

Co-authorship network of co-authors of Pablo G. Cámara

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo G. Cámara. A scholar is included among the top collaborators of Pablo G. Cámara 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 Pablo G. Cámara. Pablo G. Cámara 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.
2.
Cámara, Pablo G., et al.. (2024). Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data. Cell Reports Methods. 4(11). 100905–100905. 1 indexed citations
3.
Govek, Kiya W., et al.. (2023). CAJAL enables analysis and integration of single-cell morphological data using metric geometry. Nature Communications. 14(1). 3672–3672. 11 indexed citations
4.
Bai, Zhiliang, Steven Woodhouse, Ziran Zhao, et al.. (2022). Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL. Science Advances. 8(23). eabj2820–eabj2820. 92 indexed citations
5.
Troisi, Emma C., et al.. (2022). Pro-inflammatory cytokines mediate the epithelial-to-mesenchymal-like transition of pediatric posterior fossa ependymoma. Nature Communications. 13(1). 3936–3936. 13 indexed citations
6.
Govek, Kiya W., Sixing Chen, Yao Yao, et al.. (2022). Developmental trajectories of thalamic progenitors revealed by single-cell transcriptome profiling and Shh perturbation. Cell Reports. 41(10). 111768–111768. 9 indexed citations
7.
Bai, Zhiliang, Stefan Lundh, Dongjoo Kim, et al.. (2021). Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells. Journal for ImmunoTherapy of Cancer. 9(5). e002328–e002328. 37 indexed citations
8.
Govek, Kiya W., et al.. (2021). Single-cell transcriptomic analysis of mIHC images via antigen mapping. Science Advances. 7(10). 29 indexed citations
9.
Qiu, Qi, Peng Hu, Xiaojie Qiu, et al.. (2020). Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nature Methods. 17(10). 991–1001. 97 indexed citations
10.
Rabadán, Raúl, Tim Chu, Oliver Elliott, et al.. (2020). Identification of relevant genetic alterations in cancer using topological data analysis. Nature Communications. 11(1). 3808–3808. 30 indexed citations
11.
Ho, Yugong, Peng Hu, Michael T. Peel, et al.. (2020). Single-cell transcriptomic analysis of adult mouse pituitary reveals sexual dimorphism and physiologic demand-induced cellular plasticity. Protein & Cell. 11(8). 565–583. 57 indexed citations
12.
Govek, Kiya W., et al.. (2019). Clustering-independent analysis of genomic data using spectral simplicial theory. PLoS Computational Biology. 15(11). e1007509–e1007509. 23 indexed citations
13.
Rizvi, Abbas H., Pablo G. Cámara, Thomas J. Roberts, et al.. (2017). Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nature Biotechnology. 35(6). 551–560. 158 indexed citations
14.
Kiryluk, Krzysztof, Andrew S. Bomback, Katherine Xu, et al.. (2017). Precision Medicine for Acute Kidney Injury (AKI): Redefining AKI by Agnostic Kidney Tissue Interrogation and Genetics. Seminars in Nephrology. 38(1). 40–51. 25 indexed citations
15.
Cámara, Pablo G., Luis E. Ibáñez, & Irene Valenzuela. (2016). The String Origin of SUSY Flavor Violation. 1 indexed citations
16.
Rosenbloom, Daniel I. S., Pablo G. Cámara, Tim Chu, & Raúl Rabadán. (2016). Evolutionary scalpels for dissecting tumor ecosystems. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1867(2). 69–83. 10 indexed citations
17.
Cámara, Pablo G., Arnold J. Levine, & Raúl Rabadán. (2016). Inference of Ancestral Recombination Graphs through Topological Data Analysis. PLoS Computational Biology. 12(8). e1005071–e1005071. 49 indexed citations
18.
Cámara, Pablo G., Daniel I. S. Rosenbloom, Kevin Emmett, Arnold J. Levine, & Raúl Rabadán. (2016). Topological Data Analysis Generates High-Resolution, Genome-wide Maps of Human Recombination. Cell Systems. 3(1). 83–94. 31 indexed citations
19.
Aparicio, Luis, Pablo G. Cámara, D. G. Cerdeño, Luis E. Ibáñez, & Irene Valenzuela. (2013). The NMSSM with F-theory unified boundary conditions. Journal of High Energy Physics. 2013(2). 12 indexed citations
20.
Cámara, Pablo G. & Fernando Marchesano. (2009). Open string wavefunctions in flux compactifications. Journal of High Energy Physics. 2009(10). 17–17. 20 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

Breakdown of academic impact, for papers by Mark R. Dowling Breakdown of academic impact, for papers by María Rodríguez Martínez Breakdown of academic impact, for papers by Lindsay King Breakdown of academic impact, for papers by Michele Caselle Breakdown of academic impact, for papers by Walter Dittrich Breakdown of academic impact, for papers by Carl Herrmann Breakdown of academic impact, for papers by Markus Schulze Breakdown of academic impact, for papers by Nikodem J. Popławski Breakdown of academic impact, for papers by Michael McGuigan Breakdown of academic impact, for papers by Dipankar Ray
Rankless by CCL
2026