Hsin-Ta Wu

1.5k citations
5 papers · 211 · h-index 4

Impact in

    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research
    • Cancer Genomics and Diagnostics
    • Genomic variations and chromosomal abnormalities
    • Genomics and Rare Diseases

Papers in

    • Genomics and Phylogenetic Studies 1
    • Bioinformatics and Genomic Networks 1
    • Cancer, Lipids, and Metabolism 1
    • MicroRNA in disease regulation 1
    • Cancer-related molecular mechanisms research 1

Hsin-Ta Wu

5 papers receiving 208 citations

Peers

Hsin-Ta Wu
Comparison fields: 5 of 36
  • Cancer Research 91
  • Genetics 64
  • Molecular Biology 135
  • Reproductive Medicine 10
  • Immunology and Allergy 3
Replace Yuchao Xia with:
Yuchao Xia China
Xinzhou Ge United States
Chika Kawazu Japan
Zhiyang Xu China
Andrew Perez United States
Genevieve H. Nonet United States
Pei-Fang Tsai United States
Weixin Wu United States
Julian A. Zagalak United Kingdom
Hsin-Ta Wu relative to Yuchao Xia China Yuchao Xia's profile →
Citations per field
00.5×
Yuchao Xia · 1×
Citations per year

Countries citing papers authored by Hsin-Ta Wu

Since Specialization
Citations

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

Fields of papers citing papers by Hsin-Ta Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hsin-Ta Wu. 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 Hsin-Ta Wu. The network helps show where Hsin-Ta Wu may publish in the future.

Co-authors

The 14 scholars most cited alongside Hsin-Ta Wu, 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 Hsin-Ta Wu Line = papers co-authored together Hsin-Ta Wu links everyone, so they are left out of the graph.

All Works

About Hsin-Ta Wu

Hsin-Ta Wu is a scholar working on Molecular Biology, Cancer Research, Oncology, Genetics and Statistics and Probability, having authored 5 papers that have together received 211 indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (1 paper), Genomics and Rare Diseases (1 paper), Cancer, Lipids, and Metabolism (1 paper), MicroRNA in disease regulation (1 paper), Statistical Methods in Clinical Trials (1 paper), Genomic variations and chromosomal abnormalities (1 paper), Bioinformatics and Genomic Networks (1 paper) and Cancer-related molecular mechanisms research (1 paper). The work is most often cited by research in Cancer Research (91 citations), Genetics (64 citations), Molecular Biology (135 citations), Reproductive Medicine (10 citations) and Immunology and Allergy (3 citations). Hsin-Ta Wu has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Benjamin J. Raphael, Suzanne Sindi, Shannon MacLaughlan, Iman Hajirasouliha, Margaret M. Steinhoff, Andrew Fischer, Colin C. Collins, Laurent Brard, Daniel Miller and Peter J. Smith. Their work appears in journals such as Cancer Research, Bioinformatics, Genome biology and PLoS ONE.

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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