Ke Yuan

10.7k total citations
52 papers, 615 citations indexed

About

Ke Yuan is a scholar working on Artificial Intelligence, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Ke Yuan has authored 52 papers receiving a total of 615 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 12 papers in Molecular Biology and 9 papers in Computational Theory and Mathematics. Recurrent topics in Ke Yuan's work include Mathematics, Computing, and Information Processing (9 papers), AI in cancer detection (5 papers) and Genomics and Phylogenetic Studies (4 papers). Ke Yuan is often cited by papers focused on Mathematics, Computing, and Information Processing (9 papers), AI in cancer detection (5 papers) and Genomics and Phylogenetic Studies (4 papers). Ke Yuan collaborates with scholars based in China, United Kingdom and United States. Ke Yuan's co-authors include Florian Markowetz, Niko Beerenwinkel, Thomas Sakoparnig, Daqing Wang, Majd Zayzafoon, Omar Hameed, G P Siegal, Zhi Tang, William E. Grizzle and Donald J. Buchsbaum and has published in prestigious journals such as Nature, Nature Communications and Nano Letters.

In The Last Decade

Ke Yuan

41 papers receiving 601 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ke Yuan China	13	254	162	94	87	68	52	615
Jesse Paquette United States	11	350 1.4×	147 0.9×	58 0.6×	111 1.3×	36 0.5×	13	719
Maria Secrier United Kingdom	12	492 1.9×	98 0.6×	60 0.6×	102 1.2×	47 0.7×	27	842
Marco Chierici Italy	16	289 1.1×	98 0.6×	55 0.6×	204 2.3×	79 1.2×	39	779
Matthew Ung United States	13	521 2.1×	237 1.5×	56 0.6×	148 1.7×	64 0.9×	33	786
Konstantinos Sidiropoulos United Kingdom	9	650 2.6×	163 1.0×	69 0.7×	135 1.6×	95 1.4×	23	1.2k
Trond Hellem Bø Norway	13	626 2.5×	138 0.9×	105 1.1×	123 1.4×	86 1.3×	19	991
Kaname Kojima Japan	17	453 1.8×	95 0.6×	50 0.5×	68 0.8×	169 2.5×	48	867
Ruth Wang United States	20	753 3.0×	207 1.3×	89 0.9×	174 2.0×	58 0.9×	26	1.6k
Emmanouil Athanasiadis Greece	18	465 1.8×	87 0.5×	68 0.7×	99 1.1×	27 0.4×	42	963
Xiuhui Chen China	15	532 2.1×	298 1.8×	55 0.6×	54 0.6×	32 0.5×	35	914

Countries citing papers authored by Ke Yuan

Since Specialization

Citations

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

Fields of papers citing papers by Ke Yuan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ke Yuan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Liu, Bojing, Nicolas Coudray, Theodore Sakellaropoulos, et al.. (2025). Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer. Nature Communications. 16(1). 2328–2328. 2 indexed citations

Lytras, Spyros, Jumpei Ito, Joe Grove, et al.. (2025). Pathogen genomic surveillance and the AI revolution. Journal of Virology. 99(2). e0160124–e0160124. 4 indexed citations

Shi, Yuru, Ke Yuan, Yue Li, et al.. (2025). Clinical characteristics of single human rhinovirus infection and co-infection in the respiratory tract of children. Translational Pediatrics. 14(3). 373–381. 2 indexed citations

Powley, Ian, Cathy Richards, Hussein Uraiby, et al.. (2025). A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images. Nature Communications. 16(1). 8891–8891. 1 indexed citations

Insall, Robert H., et al.. (2025). TriDeNT : Triple deep network training for privileged knowledge distillation in histopathology. Medical Image Analysis. 102. 103479–103479.

Miller, Crispin, et al.. (2025). PLM-interact: extending protein language models to predict protein-protein interactions. Nature Communications. 16(1). 9012–9012.

Cutiongco, Marie F.A., et al.. (2025). Generating realistic single-cell images from CellProfiler representations. Medical Image Analysis. 103. 103574–103574.

Fa’ak, Faisal, Nicolas Coudray, George Jour, et al.. (2025). Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors. Clinical Cancer Research. 31(16). 3526–3536.

Yang, Zhongzhou, Wei-Chen Wu, Li Xing, et al.. (2024). Total Infectome Characterization of Respiratory Infections during the 2022–23 COVID-19 Outbreak in China Revealed Extensive Coinfections with Links to SARS-CoV-2 Status, Age, and Disease Severity. Pathogens. 13(3). 216–216.

10.

Luo, Dan, et al.. (2024). Real-Time Continuous Blood Pressure Estimation with Contact-Free Bedseismogram. PubMed. 2024. 214–219. 1 indexed citations

11.

Cheng, Cong, Ke Yuan, & Wenyang Zhang. (2024). Large Precision Matrix Estimation with Unknown Group Structure. Journal of the American Statistical Association. 120(552). 2326–2337.

12.

Orton, Richard, et al.. (2024). Mutational signature dynamics indicate SARS-CoV-2’s evolutionary capacity is driven by host antiviral molecules. PLoS Computational Biology. 20(1). e1011795–e1011795. 9 indexed citations

13.

Coudray, Nicolas, José G. Mantilla, Ke Yuan, et al.. (2024). Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Settings. Clinical Cancer Research. 31(2). 365–375. 1 indexed citations

14.

Coudray, Nicolas, Anna Yeaton, Xinyu Yang, et al.. (2024). Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nature Communications. 15(1). 4596–4596. 24 indexed citations

15.

Coudray, Nicolas, Anna Yeaton, Xinyu Yang, et al.. (2023). P1.20-06 Mapping the Histopathological Landscape of Lung Adenocarcinoma using Self-Supervised Learning Artificial Intelligence. Journal of Thoracic Oncology. 18(11). S233–S233. 1 indexed citations

16.

Manion, John, Melissa A. Musser, Min Liu, et al.. (2023). C. difficile intoxicates neurons and pericytes to drive neurogenic inflammation. Nature. 622(7983). 611–618. 35 indexed citations

17.

Macintyre, Geoff, Anna Piskorz, Edith Ross, et al.. (2021). FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections. JCO Clinical Cancer Informatics. 5(5). 176–186. 1 indexed citations

18.

Murray‐Smith, Roderick, et al.. (2020). PathologyGAN: Learning Deep Representations of Cancer Tissue. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 22 indexed citations

19.

Gao, Liangcai, Ke Yuan, Yuehan Wang, Zhuoren Jiang, & Zhi Tang. (2016). The Math Retrieval System of ICST for NTCIR-12 MathIR Task.. NTCIR. 7 indexed citations

20.

Yuan, Ke. (2005). An Improved Method of Cleaved Amplified Polymorphic Sequence (CAPS) Markers in Arabidopsis thaliana. Journal of Pharmaceutical and Biomedical Sciences. 1 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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