Nansu Zong

1.1k total citations
46 papers, 644 citations indexed

About

Nansu Zong is a scholar working on Molecular Biology, Artificial Intelligence and Information Systems. According to data from OpenAlex, Nansu Zong has authored 46 papers receiving a total of 644 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 22 papers in Artificial Intelligence and 7 papers in Information Systems. Recurrent topics in Nansu Zong's work include Biomedical Text Mining and Ontologies (19 papers), Bioinformatics and Genomic Networks (8 papers) and Semantic Web and Ontologies (8 papers). Nansu Zong is often cited by papers focused on Biomedical Text Mining and Ontologies (19 papers), Bioinformatics and Genomic Networks (8 papers) and Semantic Web and Ontologies (8 papers). Nansu Zong collaborates with scholars based in United States, South Korea and United Kingdom. Nansu Zong's co-authors include Victoria Ngo, Hyeoneui Kim, Olivier Harismendy, Andrew Wen, Hongfang Liu, Guoqian Jiang, Ming Huang, Hong‐Gee Kim, Sunyang Fu and Deepak Sharma and has published in prestigious journals such as Nature Genetics, Journal of Clinical Oncology and Bioinformatics.

In The Last Decade

Nansu Zong

43 papers receiving 625 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nansu Zong United States 14 322 201 162 98 75 46 644
Mathias Brochhausen United States 16 462 1.4× 336 1.7× 77 0.5× 107 1.1× 57 0.8× 76 710
Harold R. Solbrig United States 16 410 1.3× 382 1.9× 39 0.2× 181 1.8× 58 0.8× 71 670
Giuseppe Agapito Italy 19 442 1.4× 144 0.7× 93 0.6× 27 0.3× 154 2.1× 72 906
Alejandro Metke‐Jimenez Australia 9 217 0.7× 258 1.3× 83 0.5× 65 0.7× 31 0.4× 21 475
Meliha Yetisgen-Yildiz United States 15 468 1.5× 498 2.5× 48 0.3× 58 0.6× 38 0.5× 29 765
Dimitar Hristovski Slovenia 16 792 2.5× 519 2.6× 185 1.1× 29 0.3× 80 1.1× 41 1.1k
Luca Toldo Germany 12 489 1.5× 388 1.9× 200 1.2× 56 0.6× 52 0.7× 23 916
Jonathan Mortensen United States 9 277 0.9× 184 0.9× 50 0.3× 41 0.4× 33 0.4× 18 557
Jon Patrick Australia 15 435 1.4× 688 3.4× 38 0.2× 110 1.1× 118 1.6× 81 964
Diane E. Oliver United States 14 684 2.1× 416 2.1× 88 0.5× 284 2.9× 89 1.2× 30 1.1k

Countries citing papers authored by Nansu Zong

Since Specialization
Citations

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

Fields of papers citing papers by Nansu Zong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nansu Zong

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

All Works

20 of 20 papers shown
1.
Feng, Jun, Ahmed Abdelhameed, Jianfu Li, et al.. (2026). Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform. PubMed. 3(1). 17–17.
2.
Zong, Nansu, Liewei Wang, Pengyang Li, et al.. (2025). Advancing efficacy prediction for electronic health records based emulated trials in repurposing heart failure therapies. npj Digital Medicine. 8(1). 306–306. 1 indexed citations
3.
Li, Xiaodi, Zhe He, Rui Zhang, et al.. (2025). Synoptic reporting by summarizing cancer pathology reports using large language models. PubMed. 2(1).
4.
Kim, Yejin, et al.. (2023). Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease. Journal of the American Medical Informatics Association. 30(10). 1645–1656. 9 indexed citations
5.
Ruddy, Kathryn J., Konstantinos Leventakos, Bolun Liu, et al.. (2023). Using EHR data and machine learning approach to facilitate the identification of patients with lung cancer from a pan-cancer cohort.. Journal of Clinical Oncology. 41(16_suppl). e13552–e13552. 2 indexed citations
6.
Zong, Nansu, Andrew Wen, Sungrim Moon, et al.. (2022). Computational drug repurposing based on electronic health records: a scoping review. npj Digital Medicine. 5(1). 77–77. 30 indexed citations
7.
Huang, Ming, Andrew Wen, Huan He, et al.. (2022). Patient Portal Messaging for Asynchronous Virtual Care During the COVID-19 Pandemic: Retrospective Analysis. JMIR Human Factors. 9(2). e35187–e35187. 19 indexed citations
8.
Zhao, Yiqing, Anastasios Dimou, Feichen Shen, et al.. (2022). PO2RDF: representation of real-world data for precision oncology using resource description framework. BMC Medical Genomics. 15(1). 167–167. 2 indexed citations
9.
Zong, Nansu, Andrew Wen, Sijia Liu, et al.. (2022). Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration. Journal of Biomedical Informatics. 127. 104002–104002. 17 indexed citations
10.
Jiang, Chao, et al.. (2022). Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation. Journal of Medical Internet Research. 24(7). e38584–e38584. 6 indexed citations
11.
Na, Jie, Nansu Zong, Chen Wang, et al.. (2021). Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench. Journal of the American Medical Informatics Association. 28(11). 2313–2324. 5 indexed citations
12.
Zong, Nansu, Victoria Ngo, Andrew Wen, et al.. (2021). Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study. JMIR Medical Informatics. 9(5). e23586–e23586. 17 indexed citations
13.
Ruddy, Kathryn J., Aaron S. Mansfield, Nansu Zong, et al.. (2020). Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study. JMIR Medical Informatics. 8(6). e17353–e17353. 8 indexed citations
14.
Zong, Nansu, et al.. (2018). Tripartite Network-Based Repurposing Method Using Deep Learning to Compute Similarities for Drug-Target Prediction. Methods in molecular biology. 1903. 317–328. 10 indexed citations
15.
Zong, Nansu, et al.. (2017). xStore : Federated temporal query processing for large scale RDF triples on a cloud environment. Neurocomputing. 256. 5–12. 3 indexed citations
16.
Sansone, Susanna‐Assunta, Alejandra González-Beltrán, Philippe Rocca‐Serra, et al.. (2017). DATS, the data tag suite to enable discoverability of datasets. Scientific Data. 4(1). 170059–170059. 45 indexed citations
17.
Zong, Nansu, et al.. (2017). Constructing faceted taxonomy for heterogeneous entities based on object properties in linked data. Data & Knowledge Engineering. 112. 79–93. 1 indexed citations
18.
Ohno‐Machado, Lucila, Susanna‐Assunta Sansone, George Alter, et al.. (2017). Finding useful data across multiple biomedical data repositories using DataMed. Nature Genetics. 49(6). 816–819. 60 indexed citations
19.
Zong, Nansu, et al.. (2017). Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships. Computers in Biology and Medicine. 87. 217–229. 3 indexed citations
20.

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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