Daniel Dimitrov

3.0k total citations · 2 hit papers
13 papers, 675 citations indexed

About

Daniel Dimitrov is a scholar working on Molecular Biology, Pharmacology and Biophysics. According to data from OpenAlex, Daniel Dimitrov has authored 13 papers receiving a total of 675 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 2 papers in Pharmacology and 2 papers in Biophysics. Recurrent topics in Daniel Dimitrov's work include Single-cell and spatial transcriptomics (9 papers), Gene Regulatory Network Analysis (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Daniel Dimitrov is often cited by papers focused on Single-cell and spatial transcriptomics (9 papers), Gene Regulatory Network Analysis (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Daniel Dimitrov collaborates with scholars based in Germany, United Kingdom and Hungary. Daniel Dimitrov's co-authors include Julio Sáez-Rodríguez, Ricardo O. Ramirez Flores, Aurélien Dugourd, Pau Badia-i-Mompel, Sophia Müller‐Dott, Jana M. Braunger, Petr Tauš, Christian H. Holland, Celina Geiß and Dénes Türei and has published in prestigious journals such as Nature Communications, Nature Immunology and Nature Cell Biology.

In The Last Decade

Daniel Dimitrov

11 papers receiving 671 citations

Hit Papers

decoupleR: ensemble of computational methods to infer bio... 2022 2026 2023 2024 2022 2022 100 200 300
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.

  1. decoupleR: ensemble of computational methods to infer biological activities from omics data
    2022 302 citations Pau Badia-i-Mompel, Jana M. Braunger et al. Bioinformatics Advances profile →
  2. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data
    2022 237 citations Daniel Dimitrov, Dénes Türei et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Dimitrov Germany 7 405 177 93 77 61 13 675
Rasa Elmentaite United Kingdom 6 601 1.5× 233 1.3× 108 1.2× 127 1.6× 96 1.6× 7 790
Pau Badia-i-Mompel Germany 6 442 1.1× 136 0.8× 69 0.7× 76 1.0× 46 0.8× 9 695
Anna Arutyunyan United States 8 570 1.4× 220 1.2× 125 1.3× 134 1.7× 74 1.2× 10 825
Jayson Harshbarger Japan 5 501 1.2× 154 0.9× 105 1.1× 112 1.5× 40 0.7× 6 661
Caterina Sturtzel Austria 14 311 0.8× 111 0.6× 64 0.7× 92 1.2× 46 0.8× 31 660
Sophia Müller‐Dott Germany 6 402 1.0× 126 0.7× 66 0.7× 72 0.9× 36 0.6× 9 625
Zhuoqing Fang United States 10 396 1.0× 102 0.6× 87 0.9× 73 0.9× 21 0.3× 24 657
Cameron G. Williams Australia 3 322 0.8× 119 0.7× 64 0.7× 80 1.0× 41 0.7× 5 466
Janine Gote-Schniering Switzerland 12 337 0.8× 99 0.6× 87 0.9× 64 0.8× 48 0.8× 26 698
Nadim Aizarani Germany 4 399 1.0× 173 1.0× 100 1.1× 128 1.7× 33 0.5× 4 766

Countries citing papers authored by Daniel Dimitrov

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Dimitrov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Dimitrov

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

All Works

13 of 13 papers shown
1.
Dimitrov, Daniel, et al.. (2026). Interpretation, extrapolation and perturbation of single cells. Nature Reviews Genetics.
2.
Dimitrov, Daniel, et al.. (2025). RIDDEN: Data-driven inference of receptor activity from transcriptomic data. PLoS Computational Biology. 21(6). e1013188–e1013188.
3.
Dimitrov, Daniel, Nathalie McKay, Zbyněk Zdráhal, et al.. (2025). Unveiling the role of sex in the metabolism of indoxyl sulfate and apixaban. Scientific Reports. 15(1). 6075–6075. 3 indexed citations
4.
Dimitrov, Daniel, Christina Schmidt, Dénes Türei, et al.. (2024). MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. Briefings in Bioinformatics. 25(4). 5 indexed citations
5.
Dimitrov, Daniel, Philipp Schäfer, Pablo Rodríguez-Mier, et al.. (2024). LIANA+ provides an all-in-one framework for cell–cell communication inference. Nature Cell Biology. 26(9). 1613–1622. 48 indexed citations
6.
Baghdassarian, Hratch, Daniel Dimitrov, Erick Armingol, Julio Sáez-Rodríguez, & Nathan E. Lewis. (2024). Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. Cell Reports Methods. 4(4). 100758–100758. 6 indexed citations
7.
Schäfer, Philipp, Daniel Dimitrov, Eduardo J. Villablanca, & Julio Sáez-Rodríguez. (2024). Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nature Immunology. 25(3). 405–417. 22 indexed citations
8.
Matallana‐Surget, Sabine, Lisa M. Nigro, Lisa A. Waidner, et al.. (2024). Clarifying the murk: unveiling bacterial dynamics in response to crude oil pollution, Corexit-dispersant, and natural sunlight in the Gulf of Mexico. Frontiers in Marine Science. 10. 2 indexed citations
9.
Flores, Ricardo O. Ramirez, Jan D. Lanzer, Daniel Dimitrov, Britta Velten, & Julio Sáez-Rodríguez. (2023). Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife. 12. 25 indexed citations
10.
Dimitrov, Daniel, Dénes Türei, Martín Garrido‐Rodríguez, et al.. (2022). Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nature Communications. 13(1). 3224–3224. 237 indexed citations breakdown →
11.
Badia-i-Mompel, Pau, Jana M. Braunger, Celina Geiß, et al.. (2022). decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. 2(1). vbac016–vbac016. 302 indexed citations breakdown →
12.
Bülow, Roman D., Daniel Dimitrov, Peter Boor, & Julio Sáez-Rodríguez. (2021). How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?. Seminars in Immunopathology. 43(5). 739–752. 18 indexed citations
13.
Dimitrov, Daniel & Quan Gu. (2020). BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis. PeerJ. 8. e10469–e10469. 7 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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