Daniel Dimitrov
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
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- Immune cells in cancer
- Immune Cell Function and Interaction
Papers in
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- Single-cell and spatial transcriptomics 9
- Gene Regulatory Network Analysis 3
- Bioinformatics and Genomic Networks 2
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- Pharmacogenetics and Drug Metabolism 2
- Co-authors
- Julio Sáez-Rodríguez (10 shared papers)Ricardo O. Ramirez Flores (4 shared papers)Aurélien Dugourd (4 shared papers)Pau Badia-i-Mompel (2 shared papers)Sophia Müller‐Dott (1 shared paper)Jana M. Braunger (1 shared paper)Petr Tauš (1 shared paper)Celina Geiß (1 shared paper)
- Journals
- Cell Reports Methods (1 paper)Nature Cell Biology (1 paper)PLoS Computational Biology (1 paper)PeerJ (1 paper)Scientific Reports (1 paper)
- Partner nations
- GermanyUnited KingdomHungary
In The Last Decade
Daniel Dimitrov
11 papers receiving 792 citations
Daniel Dimitrov's Hit Papers
Peers
Comparison fields: 5 of 86
- Biophysics 57
- Immunology 156
- Molecular Biology 425
- Cancer Research 62
- Developmental Neuroscience 16
Countries citing papers authored by Daniel Dimitrov
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
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-authors
The 25 scholars most cited alongside Daniel Dimitrov, 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 | decoupleR: ensemble of computational methods to infer biological activities from omics data Hit paper breakdown → | 2022 | 363 |
| 2 | Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data Hit paper breakdown → | 2022 | 266 |
| 3 | 2024 | 64 | |
| 4 | 2024 | 27 | |
| 5 | 2023 | 26 | |
| 6 | 2021 | 20 | |
| 7 | 2024 | 9 | |
| 8 | 2024 | 9 | |
| 9 | 2020 | 7 | |
| 10 | 2024 | 3 | |
| 11 | 2025 | 3 | |
| 12 | 2026 | 0 | |
| 13 | 2025 | 0 |
About Daniel Dimitrov
Daniel Dimitrov is a scholar working on Molecular Biology, Pharmacology, Biophysics, Immunology and Biomedical Engineering, having authored 13 papers that have together received 797 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (9 papers), Gene Regulatory Network Analysis (3 papers), Pharmacogenetics and Drug Metabolism (2 papers), Bioinformatics and Genomic Networks (2 papers), Molecular Communication and Nanonetworks (2 papers), Cell Image Analysis Techniques (2 papers), Eicosanoids and Hypertension Pharmacology (1 paper) and Chronic Kidney Disease and Diabetes (1 paper). The work is most often cited by research in Biophysics (57 citations), Immunology (156 citations), Molecular Biology (425 citations), Cancer Research (62 citations) and Developmental Neuroscience (16 citations). Daniel Dimitrov has collaborated with scholars based in Germany, United Kingdom and Hungary. Frequent co-authors include Julio Sáez-Rodríguez, Ricardo O. Ramirez Flores, Aurélien Dugourd, Pau Badia-i-Mompel, Sophia Müller‐Dott, Jana M. Braunger, Petr Tauš, Celina Geiß, Christian H. Holland and Dénes Türei. Their work appears in journals such as Cell Reports Methods, Nature Cell Biology, PLoS Computational Biology, PeerJ and Scientific Reports.
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.