Min Song

171 papers and 3.2k indexed citations i.

About

Min Song is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Min Song has authored 171 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Artificial Intelligence, 74 papers in Molecular Biology and 39 papers in Information Systems. Recurrent topics in Min Song’s work include Biomedical Text Mining and Ontologies (61 papers), Advanced Text Analysis Techniques (34 papers) and Bioinformatics and Genomic Networks (31 papers). Min Song is often cited by papers focused on Biomedical Text Mining and Ontologies (61 papers), Advanced Text Analysis Techniques (34 papers) and Bioinformatics and Genomic Networks (31 papers). Min Song collaborates with scholars based in South Korea, United States and China. Min Song's co-authors include Chaomei Chen, Yoo Kyung Jeong, Ying Ding, Tamy Chambers, Reinald Kim Amplayo, Ying Ding, Erjia Yan, Su Yeon Kim, Ying Ding and Qing Xie and has published in prestigious journals such as PLoS ONE, Scientific Reports and Social Science & Medicine.

In The Last Decade

Co-authorship network of co-authors of Min Song i

Fields of papers citing papers by Min Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Min Song

Since Specialization
Citations

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

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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2025