Nan Xiao

938 citations
21 papers · 554 indexed · h-index 8
Topics
Computational Drug Discovery Methods (7 papers)Machine Learning in Bioinformatics (3 papers)Spectroscopy and Chemometric Analyses (3 papers)

In The Last Decade

Nan Xiao

16 papers receiving 543 citations

Peers

Nan Xiao
Comparison fields: 5 of 110
  • Molecular Biology 361
  • Computational Theory and Mathematics 172
  • Analytical Chemistry 84
  • Materials Chemistry 39
  • Biomedical Engineering 36
Replace Jeffrey A. Cramer with:
Jeffrey A. Cramer United States
Patrícia R. Oliveira Brazil
Haixin Ai China
Andreas Maunz Switzerland
João Paulo Ataíde Martins Brazil
Qian Xie United States
Mélaine A. Kuenemann France
Tomasz Arodź United States
Masamoto Arakawa Japan
Zheng Du China
Nan Xiao relative to Jeffrey A. Cramer United States Jeffrey A. Cramer's profile →
Citations per field
00.5×5.7×
Jeffrey A. Cramer · 1×
Citations per year

Countries citing papers authored by Nan Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Nan Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nan Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Nan Xiao. A scholar is included among the top collaborators of Nan Xiao 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 Nan Xiao. Nan Xiao 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 1
3 0
4 2
5 0
6 2
7 0
8 1
9 7
10 7
11 2
12
Voicethread Promotes Student Centered Learning by Improving Student Discussion
0
13 23
14 54
15 33
16 30
17 233
18 30
19 10
20 117

About Nan Xiao

Nan Xiao is a scholar working on Computational Theory and Mathematics, Analytical Chemistry and Toxicology, having authored 21 papers that have together received 554 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Machine Learning in Bioinformatics (3 papers) and Spectroscopy and Chemometric Analyses (3 papers). The work is most often cited by research in Computational Theory and Mathematics (172 citations), Analytical Chemistry (84 citations) and Biophysics (35 citations). Nan Xiao has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Dongsheng Cao, Qing‐Song Xu, Minfeng Zhu, Qing‐Song Xu, Alex F. Chen, Qingsong Xu, Hong‐Dong Li, Lili Wang, Xufei Wang and Yi‐Zeng Liang. Their work appears in journals such as Bioinformatics, Journal of the Science of Food and Agriculture and Colloids and Surfaces B Biointerfaces.

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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