Philipp Weiler
Impact in
- Molecular Biology top 10%
- Single-cell and spatial transcriptomics
- Gene Regulatory Network Analysis
- Genomics and Chromatin Dynamics
- Bioinformatics and Genomic Networks
- Developmental Biology and Gene Regulation
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- T-cell and B-cell Immunology
Papers in
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- Single-cell and spatial transcriptomics 7
- Gene Regulatory Network Analysis 1
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- Neuroinflammation and Neurodegeneration Mechanisms 1
- Co-authors
- Fabian Joachim Theis (4 shared papers)Marius Lange (2 shared papers)Dana Pe’er (1 shared paper)Michal Klein (1 shared paper)Mohammad Lotfollahi (1 shared paper)Aaron Streets (1 shared paper)Nir Yosef (1 shared paper)Dominik Klein (1 shared paper)
- Journals
- Nature Methods (2 papers)Cell (2 papers)Nature Reviews Genetics (1 paper)Nature Genetics (1 paper)Nature Biotechnology (1 paper)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Philipp Weiler
10 papers receiving 926 citations
Philipp Weiler's Hit Papers
Peers
Comparison fields: 5 of 73
- Molecular Biology 650
- Immunology 66
- Health Informatics 8
- Developmental Neuroscience 10
- Oncology 63
Countries citing papers authored by Philipp Weiler
This map shows the geographic impact of Philipp Weiler'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 Philipp Weiler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Weiler more than expected).
Fields of papers citing papers by Philipp Weiler
This network shows the impact of papers produced by Philipp Weiler. 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 Philipp Weiler. The network helps show where Philipp Weiler may publish in the future.
Co-authors
The 18 scholars most cited alongside Philipp Weiler, 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 | Best practices for single-cell analysis across modalities Hit paper breakdown → | 2023 | 566 |
| 2 | The scverse project provides a computational ecosystem for single-cell omics data analysis Hit paper breakdown → | 2023 | 157 |
| 3 | CellRank 2: unified fate mapping in multiview single-cell data Hit paper breakdown → | 2024 | 102 |
| 4 | 2023 | 74 | |
| 5 | 2022 | 20 | |
| 6 | 2026 | 6 | |
| 7 | 2026 | 5 | |
| 8 | 2017 | 2 | |
| 9 | 2026 | 2 | |
| 10 | 2026 | 1 |
About Philipp Weiler
Philipp Weiler is a scholar working on Molecular Biology, Neurology, Cancer Research, Oncology and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 935 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (7 papers), Gene Regulatory Network Analysis (1 paper), Cancer Genomics and Diagnostics (1 paper), Cancer Cells and Metastasis (1 paper), Robotic Mechanisms and Dynamics (1 paper), Pancreatic and Hepatic Oncology Research (1 paper), Neuroinflammation and Neurodegeneration Mechanisms (1 paper) and Robotic Path Planning Algorithms (1 paper). The work is most often cited by research in Molecular Biology (650 citations), Immunology (66 citations), Health Informatics (8 citations), Developmental Neuroscience (10 citations) and Oncology (63 citations). Philipp Weiler has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Fabian Joachim Theis, Marius Lange, Dana Pe’er, Michal Klein, Mohammad Lotfollahi, Aaron Streets, Nir Yosef, Dominik Klein, Adam Gayoso and Justin Hong. Their work appears in journals such as Nature Methods, Cell, Nature Reviews Genetics, Nature Genetics and Nature Biotechnology.
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.