Xun Zhu

2.1k total citations
23 papers, 1.3k citations indexed

About

Xun Zhu is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Xun Zhu has authored 23 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 14 papers in Cancer Research and 3 papers in Artificial Intelligence. Recurrent topics in Xun Zhu's work include Cancer-related molecular mechanisms research (11 papers), Single-cell and spatial transcriptomics (8 papers) and MicroRNA in disease regulation (6 papers). Xun Zhu is often cited by papers focused on Cancer-related molecular mechanisms research (11 papers), Single-cell and spatial transcriptomics (8 papers) and MicroRNA in disease regulation (6 papers). Xun Zhu collaborates with scholars based in United States, China and Czechia. Xun Zhu's co-authors include Lana X. Garmire, Travers Ching, Olivier Poirion, Lana X. Garmire, Cédric Arisdakessian, Hongyu Guan, Jueheng Wu, Junchao Cai, Mengfeng Li and Jie Yuan and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Investigation and Nature Communications.

In The Last Decade

Xun Zhu

21 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xun Zhu United States 14 947 556 150 122 101 23 1.3k
Yu‐Chiao Chiu United States 23 1.1k 1.1× 540 1.0× 169 1.1× 127 1.0× 95 0.9× 65 1.5k
Olivier Poirion United States 13 1.1k 1.2× 488 0.9× 210 1.4× 177 1.5× 97 1.0× 22 1.7k
Lana X. Garmire United States 10 713 0.8× 348 0.6× 163 1.1× 101 0.8× 63 0.6× 22 1.1k
Travis S. Johnson United States 16 723 0.8× 381 0.7× 167 1.1× 195 1.6× 142 1.4× 52 1.3k
Jennifer Pittman United States 12 937 1.0× 509 0.9× 96 0.6× 303 2.5× 78 0.8× 15 1.5k
Alma Andersson Sweden 11 951 1.0× 238 0.4× 131 0.9× 131 1.1× 269 2.7× 18 1.3k
Linnea Stenbeck Sweden 5 965 1.0× 255 0.5× 125 0.8× 153 1.3× 240 2.4× 7 1.2k
Rehan Akbani United States 17 1.0k 1.1× 468 0.8× 72 0.5× 399 3.3× 107 1.1× 50 1.6k
Yingtao Bi United States 20 626 0.7× 203 0.4× 76 0.5× 211 1.7× 158 1.6× 41 1.3k

Countries citing papers authored by Xun Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Xun Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xun Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Xun Zhu. A scholar is included among the top collaborators of Xun Zhu 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 Xun Zhu. Xun Zhu 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.
Chen, Lin, et al.. (2025). A comprehensive method for precisely extracting weld seam feature point in multi-interference environments. Measurement Science and Technology. 36(4). 45208–45208.
2.
Garmire, David, Xun Zhu, Qianhui Huang, et al.. (2021). GranatumX: A Community-Engaging, Modularized, and Flexible Webtool for Single-Cell Data Analysis. Genomics Proteomics & Bioinformatics. 19(3). 452–460. 4 indexed citations
3.
Wang, Cheng, et al.. (2021). Recurrent Superenhancer of the Oncogene POU5F1B in Colorectal Cancers. BioMed Research International. 2021(1). 5405060–5405060. 2 indexed citations
4.
Zhu, Xun, Thomas Wolfgruber, Matthew R. Jensen, et al.. (2021). Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women. Radiology. 301(3). 550–558. 22 indexed citations
5.
Arisdakessian, Cédric, et al.. (2019). DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome biology. 20(1). 211–211. 178 indexed citations
6.
Benny, Paula, Xun Zhu, Travers Ching, et al.. (2019). Maternal cardiovascular-related single nucleotide polymorphisms, genes, and pathways associated with early-onset preeclampsia. PLoS ONE. 14(9). e0222672–e0222672. 6 indexed citations
7.
Ching, Travers, Xun Zhu, & Lana X. Garmire. (2018). Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Computational Biology. 14(4). e1006076–e1006076. 236 indexed citations
8.
Poirion, Olivier, Xun Zhu, Travers Ching, & Lana X. Garmire. (2018). Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage. Nature Communications. 9(1). 4892–4892. 47 indexed citations
9.
Zhu, Xun, Travers Ching, Xinghua Pan, Sherman M. Weissman, & Lana X. Garmire. (2017). Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization. PeerJ. 5. e2888–e2888. 54 indexed citations
10.
Zhu, Xun, et al.. (2017). Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists. Genome Medicine. 9(1). 108–108. 50 indexed citations
11.
Poirion, Olivier, Xun Zhu, Sijia Huang, et al.. (2017). Using single‐cell multiple omics approaches to resolve tumor heterogeneity. Clinical and Translational Medicine. 6(1). 46–46. 54 indexed citations
12.
Poirion, Olivier, Xun Zhu, Travers Ching, & Lana X. Garmire. (2016). Single-Cell Transcriptomics Bioinformatics and Computational Challenges. Frontiers in Genetics. 7. 163–163. 78 indexed citations
13.
Ching, Travers, Karolina Peplowska, Sijia Huang, et al.. (2016). Pan-Cancer Analyses Reveal Long Intergenic Non-Coding RNAs Relevant to Tumor Diagnosis, Subtyping and Prognosis. EBioMedicine. 7. 62–72. 31 indexed citations
14.
Lu, Liangqun, Sara McCurdy, Sijia Huang, et al.. (2016). Time Series miRNA-mRNA integrated analysis reveals critical miRNAs and targets in macrophage polarization. Scientific Reports. 6(1). 37446–37446. 65 indexed citations
15.
Yang, Jennifer, Yoshiaki Tanaka, Montrell Seay, et al.. (2016). Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors. Nucleic Acids Research. 45(3). gkw1214–gkw1214. 34 indexed citations
16.
Wang, Lihua, Hua Yuan, Dong Hang, et al.. (2015). MicroRNA-101 polymorphisms and risk of head and neck squamous cell carcinoma in a Chinese population. Tumor Biology. 37(3). 4169–4174. 8 indexed citations
17.
Menor, Mark, Travers Ching, Xun Zhu, David Garmire, & Lana X. Garmire. (2014). mirMark: a site-level and UTR-level classifier for miRNA target prediction. Genome biology. 15(10). 500–500. 40 indexed citations
18.
Yang, Yi, Lei Liu, Junchao Cai, et al.. (2014). Targeting Smad2 and Smad3 by miR-136 Suppresses Metastasis-Associated Traits of Lung Adenocarcinoma Cells. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 21(6). 345–352. 38 indexed citations
19.
Cai, Junchao, Hongyu Guan, Lishan Fang, et al.. (2013). MicroRNA-374a activates Wnt/β-catenin signaling to promote breast cancer metastasis. Journal of Clinical Investigation. 123(2). 566–79. 305 indexed citations
20.
Zhuang, Jiahao, et al.. (2002). Design and implementation of a programmable blind equalizer for high speed multilevel data transmission. com 35. 701–704. 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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