Bo‐Han Su

577 citations
17 papers · 432 · h-index 11

Impact in

Papers in

Bo‐Han Su

16 papers receiving 425 citations

Peers

Bo‐Han Su
Comparison fields: 5 of 92
  • Computational Theory and Mathematics 279
  • Pharmacology 69
  • Molecular Biology 233
  • Pharmacology 41
  • Spectroscopy 39
Replace Sampada A. Shahane with:
Sampada A. Shahane United States
Jianlong Peng China
Britta Nisius Germany
Emilio Xavier Esposito United States
Tongan Zhao United States
Andrey A. Ivashchenko Russia
K. V. Karapetyan United States
Alexander Hillebrecht Germany
Alban Lepailleur France
Olivia A. Lin Taiwan
Bo‐Han Su relative to Sampada A. Shahane United States Sampada A. Shahane's profile →
Citations per field
00.5×1.5×
Sampada A. Shahane · 1×
Citations per year

Countries citing papers authored by Bo‐Han Su

Since Specialization
Citations

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

Fields of papers citing papers by Bo‐Han Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bo‐Han 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 Bo‐Han Su. The network helps show where Bo‐Han Su may publish in the future.

Co-authors

The 10 scholars most cited alongside Bo‐Han Su, 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 Bo‐Han Su Line = papers co-authored together Bo‐Han Su links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1 202272
2 201059
3 202139
4 201137
5 201536
6 201235
7 201629
8 201528
9 202027
10 201222
11 201715
12 201510
13 20158
14 20207
15 20175
16 20133
17 20250

About Bo‐Han Su

Bo‐Han Su is a scholar working on Computational Theory and Mathematics, Molecular Biology, Pharmacology, Pharmacology and Cardiology and Cardiovascular Medicine, having authored 17 papers that have together received 432 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (15 papers), Metabolomics and Mass Spectrometry Studies (5 papers), Pharmacogenetics and Drug Metabolism (3 papers), Receptor Mechanisms and Signaling (3 papers), Cardiac electrophysiology and arrhythmias (2 papers), Cholinesterase and Neurodegenerative Diseases (2 papers), Machine Learning in Bioinformatics (2 papers) and Bioinformatics and Genomic Networks (2 papers). The work is most often cited by research in Computational Theory and Mathematics (279 citations), Pharmacology (69 citations), Molecular Biology (233 citations), Pharmacology (41 citations) and Spectroscopy (39 citations). Bo‐Han Su has collaborated with scholars based in Taiwan and United States. Frequent co-authors include Yufeng Jane Tseng, Emilio Xavier Esposito, A. J. Hopfinger, Chien Lee, Olivia A. Lin, Chieh Lin, Kuo-Hsiang Hsu, Alex Renn, Cheng‐Fu Chou and Peihua Wang. Their work appears in journals such as Journal of Chemical Information and Modeling, Briefings in Bioinformatics, Wiley Interdisciplinary Reviews Computational Molecular Science, Bioinformatics and Journal of Cheminformatics.

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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