Thalia E. Chan
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
-
- Single-cell and spatial transcriptomics
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- CRISPR and Genetic Engineering
Papers in
-
- Single-cell and spatial transcriptomics 4
- Gene Regulatory Network Analysis 4
- Bioinformatics and Genomic Networks 1
- Pluripotent Stem Cells Research 1
- Gene expression and cancer classification 1
- Oncology 1
- Co-authors
- Michael P. H. Stumpf (4 shared papers)Ann C. Babtie (4 shared papers)Colin P. Please (1 shared paper)Ben D. MacArthur (1 shared paper)Sam Howison (1 shared paper)Andreas Schuppert (1 shared paper)Frank Müller (1 shared paper)Fumio Arai (1 shared paper)
- Journals
- Cell Systems (2 papers)Current Opinion in Systems Biology (1 paper)Nature Communications (1 paper)Methods in molecular biology (1 paper)
- Partner nations
- United KingdomNetherlandsSouth Korea
In The Last Decade
Thalia E. Chan
5 papers receiving 557 citations
Peers
Comparison fields: 5 of 62
- Biophysics 81
- Molecular Biology 491
- Cancer Research 77
- Aging 6
- Modeling and Simulation 13
Countries citing papers authored by Thalia E. Chan
This map shows the geographic impact of Thalia E. Chan'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 Thalia E. Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thalia E. Chan more than expected).
Fields of papers citing papers by Thalia E. Chan
This network shows the impact of papers produced by Thalia E. Chan. 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 Thalia E. Chan. The network helps show where Thalia E. Chan may publish in the future.
Co-authors
The 22 scholars most cited alongside Thalia E. Chan, 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 | 2017 | 339 | |
| 2 | 2017 | 114 | |
| 3 | 2019 | 87 | |
| 4 | 2017 | 19 | |
| 5 | 2019 | 3 |
About Thalia E. Chan
Thalia E. Chan is a scholar working on Molecular Biology, Oncology, Biophysics, Cancer Research and Biomedical Engineering, having authored 5 papers that have together received 562 indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (4 papers), Gene Regulatory Network Analysis (4 papers), Bioinformatics and Genomic Networks (1 paper), Cell Image Analysis Techniques (1 paper), Cancer Genomics and Diagnostics (1 paper), Pluripotent Stem Cells Research (1 paper), Gene expression and cancer classification (1 paper) and 3D Printing in Biomedical Research (1 paper). The work is most often cited by research in Biophysics (81 citations), Molecular Biology (491 citations), Cancer Research (77 citations), Aging (6 citations) and Modeling and Simulation (13 citations). Thalia E. Chan has collaborated with scholars based in United Kingdom, Netherlands and South Korea. Frequent co-authors include Michael P. H. Stumpf, Ann C. Babtie, Colin P. Please, Ben D. MacArthur, Sam Howison, Andreas Schuppert, Frank Müller, Fumio Arai, Patrick S. Stumpf and Rosanna C. G. Smith. Their work appears in journals such as Cell Systems, Current Opinion in Systems Biology, Nature Communications and Methods in molecular biology.
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.