Jian‐Yu Shi

2.6k citations
81 papers · 1.8k indexed · h-index 23
Topics
Computational Drug Discovery Methods (41 papers)Machine Learning in Bioinformatics (18 papers)Bioinformatics and Genomic Networks (16 papers)
Journals
Nature CommunicationsSHILAP Revista de lepidopterologíaBioinformatics

In The Last Decade

Jian‐Yu Shi

76 papers receiving 1.8k citations

Peers

Jian‐Yu Shi
Comparison fields: 5 of 136
  • Molecular Biology 1.2k
  • Computational Theory and Mathematics 1.0k
  • Materials Chemistry 374
  • Artificial Intelligence 195
  • Pharmacology 173
Replace Kimberley M. Zorn with:
Kimberley M. Zorn United States
Thomas R. Lane United States
Elena Cibrián–Uhalte Germany
Xutong Li China
Floriane Montanari Austria
Daniel H. Foil United States
Feisheng Zhong China
Nadine Schneider Germany
Konda Mani Saravanan India
Dingyan Wang China
Jian‐Yu Shi relative to Kimberley M. Zorn United States Kimberley M. Zorn's profile →
Citations per field
00.5×
Kimberley M. Zorn · 1×
Citations per year

Countries citing papers authored by Jian‐Yu Shi

Since Specialization
Citations

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

Fields of papers citing papers by Jian‐Yu Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jian‐Yu Shi. 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 Jian‐Yu Shi. The network helps show where Jian‐Yu Shi may publish in the future.

Co-authorship network of co-authors of Jian‐Yu Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Jian‐Yu Shi. A scholar is included among the top collaborators of Jian‐Yu Shi 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 Jian‐Yu Shi. Jian‐Yu Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 4
2 4
3 9
4 2
5 4
6 1
7 7
8 9
9 9
10 6
11 12
12 14
13 11
14 15
15 15
16 30
17 23
18 55
19 8
20
Local Phase Quantization Texture Descriptor for Protein Classification.
7

About Jian‐Yu Shi

Jian‐Yu Shi is a scholar working on Computational Theory and Mathematics, Computational Mathematics and Molecular Biology, having authored 81 papers that have together received 1.8k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (41 papers), Machine Learning in Bioinformatics (18 papers) and Bioinformatics and Genomic Networks (16 papers). The work is most often cited by research in Computational Theory and Mathematics (1.0k citations), Toxicology (70 citations) and Pharmacology (173 citations). Jian‐Yu Shi has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Hui Yu, Siu‐Ming Yiu, Shao‐Wu Zhang, Jian Feng, Quan Pan, Zun Liu, Huang Zhang, Yongmei Cheng, Hua Huang and Yanning Zhang. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Bioinformatics.

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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