Winnie Tan

626 citations
16 papers · 443 · h-index 11

Impact in

    • DNA Repair Mechanisms
    • CRISPR and Genetic Engineering
    • Ubiquitin and proteasome pathways
    • Genomics and Chromatin Dynamics
    • RNA modifications and cancer
    • Epigenetics and DNA Methylation

Papers in

    • DNA Repair Mechanisms 11
    • CRISPR and Genetic Engineering 3
    • Genomics and Chromatin Dynamics 2
    • DNA and Nucleic Acid Chemistry 2
    • Ubiquitin and proteasome pathways 2
    • Cancer therapeutics and mechanisms 1
    • Microtubule and mitosis dynamics 4

Winnie Tan

16 papers receiving 441 citations

Peers

Winnie Tan
Comparison fields: 5 of 57
  • Cancer Research 80
  • Molecular Biology 357
  • Cell Biology 57
  • Oncology 75
  • Genetics 51
Replace Sree Rama Chaitanya Sridhara with:
Sree Rama Chaitanya Sridhara Portugal
Lan Chen China
Pavan Kumar Dhanyamraju United States
Aïda Ghoul France
Sreeja C. Sekhar United States
Carolina Meloni Vicente Brazil
Debbie Hall United Kingdom
Gaute Brede Norway
Maristella Maggi Italy
Winnie Tan relative to Sree Rama Chaitanya Sridhara Portugal Sree Rama Chaitanya Sridhara's profile →
Citations per field
00.5×
Sree Rama Chaitanya Sridhara · 1×
Citations per year

Countries citing papers authored by Winnie Tan

Since Specialization
Citations

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

Fields of papers citing papers by Winnie Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Winnie Tan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Winnie Tan Line = papers co-authored together Winnie Tan links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2016101
2 201466
3 202055
4 201444
5 202027
6 200227
7 202025
8 202219
9 201718
10 201517
11 202012
12 202010
13 20208
14 20208
15 20215
16 20251

About Winnie Tan

Winnie Tan is a scholar working on Molecular Biology, Cell Biology, Oncology, Genetics and Cancer Research, having authored 16 papers that have together received 443 indexed citations. Recurring topics across this work include DNA Repair Mechanisms (11 papers), Microtubule and mitosis dynamics (4 papers), CRISPR and Genetic Engineering (3 papers), Genomics and Chromatin Dynamics (2 papers), DNA and Nucleic Acid Chemistry (2 papers), Ubiquitin and proteasome pathways (2 papers), Hepatocellular Carcinoma Treatment and Prognosis (1 paper) and Cancer therapeutics and mechanisms (1 paper). The work is most often cited by research in Cancer Research (80 citations), Molecular Biology (357 citations), Cell Biology (57 citations), Oncology (75 citations) and Genetics (51 citations). Winnie Tan has collaborated with scholars based in Australia, Hong Kong and China. Frequent co-authors include Andrew J. Deans, Vincent J. Murphy, Sylvie van Twest, Wayne Crismani, Jörg Heierhorst, Julienne J. O’Rourke, Charlotte Hodson, Kin Chan, Nikki P. Lee and Paolo Swuec. Their work appears in journals such as Molecular and Cellular Biology, Scientific Reports, Progress in Biophysics and Molecular Biology, Oncology Reports and Frontiers in Cell and Developmental 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.

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