Xiaojun Yao

3.2k citations
99 papers · 1.9k indexed · 1 hit paper · h-index 23
Topics
Computational Drug Discovery Methods (26 papers)Protein Structure and Dynamics (11 papers)Machine Learning in Materials Science (11 papers)
Partner nations
MacaoChinaUnited Kingdom

In The Last Decade

Xiaojun Yao

92 papers receiving 1.9k citations

Hit Papers

Luteolin and its derivative apigenin suppress the inducib...2021202620222024202150100150

Peers

Xiaojun Yao
Comparison fields: 5 of 131
  • Molecular Biology 989
  • Computational Theory and Mathematics 303
  • Oncology 255
  • Pharmacology 240
  • Cancer Research 214
Replace Saleha Anwar with:
Saleha Anwar India
Pankaj Kumar Singh India
Vivek Asati India
Nam Doo Kim South Korea
Somdutt Mujwar India
Parvez Κhan India
Maija Lahtela‐Kakkonen Finland
Onat Kadioglu Germany
Zunnan Huang China
Jingkang Shen China
Xiaojun Yao relative to Saleha Anwar India Saleha Anwar's profile →
Citations per field
00.5×4.3×
Saleha Anwar · 1×
Citations per year

Countries citing papers authored by Xiaojun Yao

Since Specialization
Citations

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

Fields of papers citing papers by Xiaojun Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaojun Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojun Yao. A scholar is included among the top collaborators of Xiaojun Yao 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 Xiaojun Yao. Xiaojun Yao 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 0
2 4
3 0
4 3
5 4
6 0
7 4
8 10
9 1
10 0
11 6
12 14
13 22
14 12
15 23
16 19
17 45
18 42
19 21
20 52

About Xiaojun Yao

Xiaojun Yao is a scholar working on Computational Theory and Mathematics, Complementary and alternative medicine and Molecular Biology, having authored 99 papers that have together received 1.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (26 papers), Protein Structure and Dynamics (11 papers) and Machine Learning in Materials Science (11 papers). The work is most often cited by research in Computational Theory and Mathematics (303 citations), Molecular Biology (989 citations) and Biochemistry (86 citations). Xiaojun Yao has collaborated with scholars based in Macao, China and United Kingdom. Frequent co-authors include Elaine Lai‐Han Leung, Xing‐Xing Fan, Qibiao Wu, Zebo Jiang, Liang Liu, Chun Xie, Ying Xie, Yuwei Wang, Liang Liu and Jumin Huang. Their work appears in journals such as Nature Communications, Environmental Science & Technology and Analytical Chemistry.

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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