Runyu Hong

4.1k total citations
6 papers, 122 citations indexed

About

Runyu Hong is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Runyu Hong has authored 6 papers receiving a total of 122 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Molecular Biology. Recurrent topics in Runyu Hong's work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and RNA modifications and cancer (1 paper). Runyu Hong is often cited by papers focused on AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and RNA modifications and cancer (1 paper). Runyu Hong collaborates with scholars based in United States. Runyu Hong's co-authors include David Fenyö, Narges Razavian, Deborah F. DeLair, Douglas M. Donnelly, Aristotelis Tsirigos, George Jour, Nicolas Coudray, Sofia Nomikou, Una Moran and Theodore Sakellaropoulos and has published in prestigious journals such as Cancer Research, Journal of Investigative Dermatology and Cell Reports Medicine.

In The Last Decade

Runyu Hong

6 papers receiving 122 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Runyu Hong United States	4	61	61	31	28	19	6	122
Birgid Schömig‐Markiefka Germany	7	84 1.4×	87 1.4×	54 1.7×	66 2.4×	30 1.6×	17	226
Zheng Jing China	4	63 1.0×	62 1.0×	44 1.4×	18 0.6×	87 4.6×	9	230
Kruthi Suvarna Japan	7	54 0.9×	69 1.1×	32 1.0×	28 1.0×	88 4.6×	11	237
Brie Kezlarian United States	8	40 0.7×	37 0.6×	7 0.2×	25 0.9×	24 1.3×	16	133
Vincent Peter C. Magboo Philippines	8	30 0.5×	65 1.1×	21 0.7×	43 1.5×	21 1.1×	27	206
Didem Çifçi Germany	5	103 1.7×	113 1.9×	37 1.2×	45 1.6×	15 0.8×	10	167
Yongai Li China	9	45 0.7×	231 3.8×	16 0.5×	25 0.9×	9 0.5×	25	314
Sami Tabbarah Canada	6	100 1.6×	142 2.3×	44 1.4×	25 0.9×	19 1.0×	9	190
Ayesha Azam United Kingdom	4	144 2.4×	118 1.9×	42 1.4×	86 3.1×	31 1.6×	7	236

Countries citing papers authored by Runyu Hong

Since Specialization

Citations

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

Fields of papers citing papers by Runyu Hong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Runyu Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Runyu Hong. A scholar is included among the top collaborators of Runyu Hong 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 Runyu Hong. Runyu Hong 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:

6 of 6 papers shown

1.

Wang, Joshua, Runyu Hong, Jimin Tan, Wenke Liu, & David Fenyö. (2024). Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration. Cancer Research. 84(6_Supplement). 888–888. 1 indexed citations

2.

Hong, Runyu & David Fenyö. (2022). Deep Learning and Its Applications in Computational Pathology. BioMedInformatics. 2(1). 159–168. 10 indexed citations

3.

Hong, Runyu, et al.. (2021). Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Reports Medicine. 2(9). 100400–100400. 80 indexed citations

4.

Hong, Runyu, et al.. (2021). Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning. BioMedInformatics. 2(1). 101–105. 3 indexed citations

5.

Kim, Randie H., Sofia Nomikou, Nicolas Coudray, et al.. (2021). Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas. Journal of Investigative Dermatology. 142(6). 1650–1658.e6. 27 indexed citations

6.

Wang, Liang-Bo, Alla Karpova, Marina Gritsenko, et al.. (2021). Abstract 2170: Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Research. 81(13_Supplement). 2170–2170. 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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