Guan-Hua Su

831 citations
13 papers · 405 indexed · h-index 8
Topics
Radiomics and Machine Learning in Medical Imaging (9 papers)Cancer Genomics and Diagnostics (6 papers)Ferroptosis and cancer prognosis (6 papers)
Journals
SHILAP Revista de lepidopterologíaScience AdvancesSignal Transduction and Targeted Therapy

In The Last Decade

Guan-Hua Su

13 papers receiving 404 citations

Peers

Guan-Hua Su
Comparison fields: 5 of 70
  • Radiology, Nuclear Medicine and Imaging 185
  • Oncology 110
  • Molecular Biology 101
  • Pulmonary and Respiratory Medicine 90
  • Cancer Research 82
Replace Siteng Chen with:
Siteng Chen China
Naresh Prodduturi United States
Simon Schallenberg Germany
Geunwon Kim United States
Irineu Illa-Bochaca United States
Huan-Chang Liang Austria
Mika Sarkin Jain United States
Laura Roccograndi United States
Xiangwei Ge China
Kaneschka Yaqubi Germany
Guan-Hua Su relative to Siteng Chen China Siteng Chen's profile →
Citations per field
00.5×1.5×1.9×
Siteng Chen · 1×
Citations per year

Countries citing papers authored by Guan-Hua Su

Since Specialization
Citations

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

Fields of papers citing papers by Guan-Hua Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guan-Hua Su. 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 Guan-Hua Su. The network helps show where Guan-Hua Su may publish in the future.

Co-authorship network of co-authors of Guan-Hua Su

This figure shows the co-authorship network connecting the top 25 collaborators of Guan-Hua Su. A scholar is included among the top collaborators of Guan-Hua Su based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guan-Hua Su. Guan-Hua Su is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
#WorkIndexed citations
1 39
2 10
3 25
4 58
5 3
6 5
7 6
8 60
9 44
10 2
11 7
12 82
13 64

About Guan-Hua Su

Guan-Hua Su is a scholar working on Health Informatics, Cancer Research and Radiology, Nuclear Medicine and Imaging, having authored 13 papers that have together received 405 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Cancer Genomics and Diagnostics (6 papers) and Ferroptosis and cancer prognosis (6 papers). The work is most often cited by research in Health Informatics (13 citations), Radiology, Nuclear Medicine and Imaging (185 citations) and Cancer Research (82 citations). Guan-Hua Su has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Zhi-Ming Shao, Yi‐Zhou Jiang, Chao You, Yi Xiao, He Wang, Rencheng Zheng, Yi Xiao, Ying Xu, Ding Ma and Yajia Gu. Their work appears in journals such as SHILAP Revista de lepidopterología, Science Advances and Signal Transduction and Targeted Therapy.

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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