Baifen Song

64 total papers · 576 total citations
43 papers, 291 citations indexed

About

Baifen Song is a scholar working on Infectious Diseases, Molecular Biology and Immunology. According to data from OpenAlex, Baifen Song has authored 43 papers receiving a total of 291 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Infectious Diseases, 15 papers in Molecular Biology and 13 papers in Immunology. Recurrent topics in Baifen Song's work include Antimicrobial Resistance in Staphylococcus (11 papers), Antimicrobial Peptides and Activities (8 papers) and Immune Response and Inflammation (7 papers). Baifen Song is often cited by papers focused on Antimicrobial Resistance in Staphylococcus (11 papers), Antimicrobial Peptides and Activities (8 papers) and Immune Response and Inflammation (7 papers). Baifen Song collaborates with scholars based in China. Baifen Song's co-authors include Yudong Cui, Lijie Tang, Jinzhu Ma, Yijing Li, Zhanbo Zhu, Liyang Li, Zhe Liu, Hu‐Nan Sun, Xintong Wang and Di Yao and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Applied and Environmental Microbiology.

In The Last Decade

Baifen Song

36 papers receiving 286 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Baifen Song 117 86 71 44 34 43 291
Tereza Almeida 69 0.6× 53 0.6× 78 1.1× 22 0.5× 41 1.2× 27 250
Falong Yang 119 1.0× 30 0.3× 45 0.6× 51 1.2× 33 1.0× 40 292
Ky V. Hoang 141 1.2× 84 1.0× 52 0.7× 29 0.7× 97 2.9× 19 317
Rujian Cai 131 1.1× 107 1.2× 28 0.4× 57 1.3× 30 0.9× 22 258
Timothy W. J. Olchowy 39 0.3× 67 0.8× 54 0.8× 67 1.5× 64 1.9× 29 324
Shilei Dong 102 0.9× 33 0.4× 29 0.4× 35 0.8× 60 1.8× 29 310
Javier Asín 51 0.4× 93 1.1× 37 0.5× 13 0.3× 16 0.5× 46 329
Rose Mushin 73 0.6× 31 0.4× 40 0.6× 79 1.8× 42 1.2× 31 267
Lianci Peng 173 1.5× 43 0.5× 104 1.5× 101 2.3× 25 0.7× 32 327
Fernanda Raya Tonetti 107 0.9× 86 1.0× 62 0.9× 19 0.4× 82 2.4× 24 315

Countries citing papers authored by Baifen Song

Since Specialization
Citations

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

Fields of papers citing papers by Baifen Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baifen Song

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

All Works

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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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2026