Kai Song

2.0k citations
85 papers · 1.1k indexed · 2 hit papers · h-index 16
Topics
Genomics and Phylogenetic Studies (13 papers)RNA modifications and cancer (11 papers)Colorectal Cancer Treatments and Studies (10 papers)
Journals
SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE

In The Last Decade

Kai Song

80 papers receiving 1.1k citations

Hit Papers

Identifying viruses from metagenomic data using deep lear...202020262022202420202023100200300

Peers

Kai Song
Comparison fields: 5 of 127
  • Molecular Biology 579
  • Ecology 354
  • Plant Science 147
  • Cancer Research 136
  • Oncology 126
Replace Chao Deng with:
Chao Deng China
Zhiheng Liu China
Arif Uddin India
Guoqing Huang China
Fengying Gao China
Chunyuan Yang China
Ron Ammar United States
Parul Mittal Australia
Teng Huang China
Yo Suzuki United States
Kai Song relative to Chao Deng China Chao Deng's profile →
Citations per field
00.5×1.5×
Chao Deng · 1×
Citations per year

Countries citing papers authored by Kai Song

Since Specialization
Citations

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

Fields of papers citing papers by Kai Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Song

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Song. A scholar is included among the top collaborators of Kai Song 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 Kai Song. Kai Song 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 2
3 1
4 8
5 18
6
A randomized, controlled clinical trial comparing remimazolam to propofol when combined with alfentanil for sedation during ERCP proceduresbreakdown →
55
7 1
8 8
9 2
10 6
11 2
12 19
13 5
14 5
15 3
16 6
17 40
18 12
19 2
20 5

About Kai Song

Kai Song is a scholar working on Cancer Research, Structural Biology and Molecular Biology, having authored 85 papers that have together received 1.1k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (13 papers), RNA modifications and cancer (11 papers) and Colorectal Cancer Treatments and Studies (10 papers). The work is most often cited by research in Ecology (354 citations), Cancer Research (136 citations) and Anesthesiology and Pain Medicine (49 citations). Kai Song has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Fengzhu Sun, Nathan A. Ahlgren, Yi Li, Chao Deng, Xiaohui Xie, Ryan Poplin, Jie Ren, Jed A. Fuhrman, Jie Ren and Minghua Deng. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

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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