Doyeon Ha

442 total citations · 1 hit paper
5 papers, 307 citations indexed

About

Doyeon Ha is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Doyeon Ha has authored 5 papers receiving a total of 307 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 3 papers in Oncology and 2 papers in Computational Theory and Mathematics. Recurrent topics in Doyeon Ha's work include Cancer Immunotherapy and Biomarkers (2 papers), Bioinformatics and Genomic Networks (2 papers) and Computational Drug Discovery Methods (2 papers). Doyeon Ha is often cited by papers focused on Cancer Immunotherapy and Biomarkers (2 papers), Bioinformatics and Genomic Networks (2 papers) and Computational Drug Discovery Methods (2 papers). Doyeon Ha collaborates with scholars based in South Korea and France. Doyeon Ha's co-authors include Sanguk Kim, Kunyoo Shin, Donghyo Kim, Seong Kyu Han, Heetak Lee, JungHo Kong, Juhun Lee and Sin‐Hyeog Im and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Science Advances.

In The Last Decade

Doyeon Ha

5 papers receiving 303 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Network-based machine learning approach to predict immunotherapy response in cancer patients

2022 126 citations JungHo Kong, Doyeon Ha et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Doyeon Ha South Korea	5	136	119	66	61	41	5	307
Masturah Bte Mohd Abdul Rashid Singapore	9	105 0.8×	166 1.4×	65 1.0×	85 1.4×	24 0.6×	19	371
JungHo Kong South Korea	7	99 0.7×	99 0.8×	43 0.7×	18 0.3×	33 0.8×	13	229
Matthew McCoy United States	12	217 1.6×	163 1.4×	104 1.6×	33 0.5×	40 1.0×	32	439
Manuel Nietert Germany	11	125 0.9×	108 0.9×	27 0.4×	44 0.7×	111 2.7×	33	390
Connor O’Leary Australia	10	178 1.3×	147 1.2×	106 1.6×	31 0.5×	155 3.8×	29	387
Angela Sandru Romania	8	159 1.2×	155 1.3×	36 0.5×	18 0.3×	38 0.9×	11	299
Liantao Guo China	7	121 0.9×	92 0.8×	76 1.2×	18 0.3×	55 1.3×	14	266
Musalula Sinkala Zambia	10	107 0.8×	188 1.6×	122 1.8×	13 0.2×	59 1.4×	29	380

Countries citing papers authored by Doyeon Ha

Since Specialization

Citations

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

Fields of papers citing papers by Doyeon Ha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Doyeon Ha

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

All Works

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5 of 5 papers shown

1.

Lee, Juhun, Donghyo Kim, JungHo Kong, et al.. (2024). Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors. Science Advances. 10(5). eadj0785–eadj0785. 12 indexed citations

2.

Kong, JungHo, Doyeon Ha, Juhun Lee, et al.. (2022). Network-based machine learning approach to predict immunotherapy response in cancer patients. Nature Communications. 13(1). 3703–3703. 126 indexed citations breakdown →

3.

Ha, Doyeon, et al.. (2022). Development of bioinformatics and multi-omics analyses in organoids. BMB Reports. 56(1). 43–48. 14 indexed citations

4.

Ha, Doyeon, et al.. (2022). Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes. Nucleic Acids Research. 50(4). 1849–1863. 7 indexed citations

5.

Lee, Heetak, Donghyo Kim, Seong Kyu Han, et al.. (2020). Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nature Communications. 11(1). 5485–5485. 148 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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