Masato Kimura

1.7k total citations
2 papers, 324 citations indexed

About

Masato Kimura is a scholar working on Molecular Biology, Information Systems and Management and Genetics. According to data from OpenAlex, Masato Kimura has authored 2 papers receiving a total of 324 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Molecular Biology, 1 paper in Information Systems and Management and 1 paper in Genetics. Recurrent topics in Masato Kimura's work include Biomedical Text Mining and Ontologies (2 papers), Metabolomics and Mass Spectrometry Studies (1 paper) and Genetic Associations and Epidemiology (1 paper). Masato Kimura is often cited by papers focused on Biomedical Text Mining and Ontologies (2 papers), Metabolomics and Mass Spectrometry Studies (1 paper) and Genetic Associations and Epidemiology (1 paper). Masato Kimura collaborates with scholars based in United States. Masato Kimura's co-authors include Luning Hao, Nataliya Sharopova, Michael Feolo, Н. В. Попова, K. A. Tryka, Moira Lee, Lora Ziyabari, Yumi Jin, Zhen Y Wang and Anne Sturcke and has published in prestigious journals such as Nucleic Acids Research and Scientific Data.

In The Last Decade

Masato Kimura

2 papers receiving 319 citations

Peers

Masato Kimura
Anne Sturcke United States
Nataliya Sharopova United States
Lora Ziyabari United States
Moira Lee United States
Karen He United States
Katrina Learned United States
Joanna Zhuang United Kingdom
Nicholas E. Hardison United States
Anne Sturcke United States
Masato Kimura
Citations per year, relative to Masato Kimura Masato Kimura (= 1×) peers Anne Sturcke

Countries citing papers authored by Masato Kimura

Since Specialization
Citations

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

Fields of papers citing papers by Masato Kimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Masato Kimura

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

All Works

2 of 2 papers shown
1.
Pan, Huaqin, Stephen W. Erickson, Michael Feolo, et al.. (2022). Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX. Scientific Data. 9(1). 532–532. 3 indexed citations
2.
Tryka, K. A., Luning Hao, Anne Sturcke, et al.. (2013). NCBI’s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Research. 42(D1). D975–D979. 321 indexed citations

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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