Yunku Yeu

52 total papers · 537 total citations
14 papers, 354 citations indexed

About

Yunku Yeu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Oncology. According to data from OpenAlex, Yunku Yeu has authored 14 papers receiving a total of 354 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 2 papers in Oncology. Recurrent topics in Yunku Yeu's work include Bioinformatics and Genomic Networks (5 papers), Machine Learning in Bioinformatics (3 papers) and Computational Drug Discovery Methods (3 papers). Yunku Yeu is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), Machine Learning in Bioinformatics (3 papers) and Computational Drug Discovery Methods (3 papers). Yunku Yeu collaborates with scholars based in South Korea and United States. Yunku Yeu's co-authors include Sanghyun Park, Tae Hyun Hwang, Youngmi Yoon, Jean R. Clemenceau, James Kim, Rubina Tuladhar, David H. Mathews, Lawrence Lum, Zhen Tan and John T. Piazza and has published in prestigious journals such as Nature Communications, Cancer Research and Information Sciences.

In The Last Decade

Yunku Yeu

13 papers receiving 344 citations

Author Peers

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

Author						Papers	Cites
Yunku Yeu	223	71	64	59	29	14	354
Lu Meng	299 1.3×	38 0.5×	38 0.6×	50 0.8×	27 0.9×	12	392
Thilde Terkelsen	277 1.2×	33 0.5×	45 0.7×	19 0.3×	28 1.0×	18	378
Anna Meyfour	184 0.8×	59 0.8×	70 1.1×	20 0.3×	22 0.8×	28	375
Masato Ooka	218 1.0×	24 0.3×	74 1.2×	43 0.7×	24 0.8×	22	395
Branislav Mišovic	275 1.2×	41 0.6×	56 0.9×	12 0.2×	26 0.9×	8	381
Lorena Leonardelli	191 0.9×	63 0.9×	48 0.8×	20 0.3×	72 2.5×	14	310
Neil Hewitt	273 1.2×	30 0.4×	89 1.4×	41 0.7×	19 0.7×	11	367
Lindsay Smith	207 0.9×	34 0.5×	44 0.7×	58 1.0×	7 0.2×	12	369
Vincent Frappier	300 1.3×	39 0.5×	25 0.4×	48 0.8×	14 0.5×	12	372
M Koyama	193 0.9×	51 0.7×	87 1.4×	20 0.3×	31 1.1×	17	380

Countries citing papers authored by Yunku Yeu

Since Specialization

Citations

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

Fields of papers citing papers by Yunku Yeu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yunku Yeu

This figure shows the co-authorship network connecting the top 25 collaborators of Yunku Yeu. A scholar is included among the top collaborators of Yunku Yeu 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 Yunku Yeu. Yunku Yeu 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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