Shijia Zhu

2.4k total citations · 1 hit paper
28 papers, 987 citations indexed

About

Shijia Zhu is a scholar working on Molecular Biology, Epidemiology and Genetics. According to data from OpenAlex, Shijia Zhu has authored 28 papers receiving a total of 987 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 7 papers in Epidemiology and 7 papers in Genetics. Recurrent topics in Shijia Zhu's work include Liver Disease Diagnosis and Treatment (5 papers), Epigenetics and DNA Methylation (5 papers) and Hepatocellular Carcinoma Treatment and Prognosis (3 papers). Shijia Zhu is often cited by papers focused on Liver Disease Diagnosis and Treatment (5 papers), Epigenetics and DNA Methylation (5 papers) and Hepatocellular Carcinoma Treatment and Prognosis (3 papers). Shijia Zhu collaborates with scholars based in United States, China and Japan. Shijia Zhu's co-authors include Gang Fang, Andrew Xiao, Tao Wu, Yifei Liu, Lawrence Hon, Matthew G. Seetin, Mei Zhong, James A. Swenberg, Guilin Wang and Yongquan Lai and has published in prestigious journals such as Nature, Cell and Nature Communications.

In The Last Decade

Shijia Zhu

25 papers receiving 976 citations

Hit Papers

DNA methylation on N6-adenine in mammalian embryonic stem... 2016 2026 2019 2022 2016 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shijia Zhu United States 11 791 105 85 83 82 28 987
О. А. Донцова Russia 17 859 1.1× 122 1.2× 49 0.6× 66 0.8× 100 1.2× 82 1.1k
Amy J. Malhowski United States 9 800 1.0× 215 2.0× 49 0.6× 80 1.0× 112 1.4× 9 1.1k
Kam D Dahlquist United States 7 1.0k 1.3× 176 1.7× 70 0.8× 30 0.4× 90 1.1× 16 1.3k
Benjamin C. Orsburn United States 15 537 0.7× 105 1.0× 72 0.8× 85 1.0× 54 0.7× 47 813
Feng Gong United States 21 1.1k 1.4× 308 2.9× 52 0.6× 64 0.8× 89 1.1× 52 1.3k
Shahid Banday United States 10 506 0.6× 101 1.0× 45 0.5× 74 0.9× 125 1.5× 18 868
Sailu Yellaboina India 14 565 0.7× 95 0.9× 35 0.4× 70 0.8× 40 0.5× 24 729
Andrew F. Jarnuczak United Kingdom 10 888 1.1× 79 0.8× 44 0.5× 59 0.7× 82 1.0× 15 1.2k
Jinghui Zhang China 9 362 0.5× 63 0.6× 51 0.6× 70 0.8× 61 0.7× 15 559
Jens C. Schmidt United States 16 1.1k 1.4× 131 1.2× 34 0.4× 132 1.6× 46 0.6× 31 1.4k

Countries citing papers authored by Shijia Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Shijia Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shijia Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Shijia Zhu. A scholar is included among the top collaborators of Shijia 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 Shijia Zhu. Shijia 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.
Ryu, Joohyun, Mihir Shetty, Pádraig D’Arcy, et al.. (2025). Proteomic Analysis of ARID1A-Deficient Ovarian Clear Cell Carcinoma Cells Reveals Differential Mitochondria ETC Subunit Abundances and Targetable Mitochondrial Pathways. International Journal of Molecular Sciences. 26(12). 5466–5466.
3.
Yan, Libo, et al.. (2025). Efficiency improvement of in vitro chromosome doubling in melon haploid. Plant Cell Tissue and Organ Culture (PCTOC). 160(2).
4.
Zhang, Xiaowan, et al.. (2025). An Efficient Rice Virus-Induced Gene Silencing System Mediated by Wheat Dwarf Virus. Applied Sciences. 15(11). 5818–5818.
5.
Zhu, Shijia, Naoto Kubota, Shidan Wang, et al.. (2024). STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics. Nature Communications. 15(1). 7559–7559. 4 indexed citations
6.
Hu, Li, et al.. (2024). Global, regional, and national burden of cutaneous malignant melanoma from 1990 to 2021 and prediction to 2045. Frontiers in Oncology. 14. 1512942–1512942. 5 indexed citations
7.
Fujiwara, Naoto, Naoto Kubota, Shijia Zhu, et al.. (2023). Disseminative Recurrence Signature for Hepatocellular Carcinoma From Nonalcoholic Fatty Liver Disease. SHILAP Revista de lepidopterología. 2(5). 681–683. 3 indexed citations
8.
Wang, Zixi, Shijia Zhu, Yuemeng Jia, et al.. (2023). Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease. Cell. 186(9). 1968–1984.e20. 41 indexed citations
9.
Huang, Tinghua, Mingjiang Xu, Jinhui Liu, et al.. (2020). A transcriptional landscape of 28 porcine tissues obtained by super deepSAGE sequencing. BMC Genomics. 21(1). 229–229. 7 indexed citations
10.
Qian, Tongqi, Shijia Zhu, Zhibin Zhu, et al.. (2020). Restricted immunological and cellular pathways are shared by murine models of chronic alcohol consumption. Scientific Reports. 10(1). 2451–2451. 8 indexed citations
11.
Yip, Shun H., et al.. (2019). MPIC: Molecular Prognostic Indicators in Cirrhosis Database for Clinical Context-Specific in Silico Prognostic Biomarker Validation. Frontiers in Genetics. 10. 830–830. 1 indexed citations
12.
Zhu, Shijia, John Beaulaurier, Gintaras Deikus, et al.. (2018). Mapping and characterizing N6-methyladenine in eukaryotic genomes using single-molecule real-time sequencing. Genome Research. 28(7). 1067–1078. 67 indexed citations
13.
Zhu, Shijia & Gang Fang. (2018). MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment. Bioinformatics. 34(14). 2341–2348. 12 indexed citations
14.
Beaulaurier, John, Shijia Zhu, Gintaras Deikus, et al.. (2017). Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation. Nature Biotechnology. 36(1). 61–69. 107 indexed citations
15.
Wu, Tao, Tao Wang, Matthew G. Seetin, et al.. (2016). DNA methylation on N6-adenine in mammalian embryonic stem cells. Nature. 532(7599). 329–333. 462 indexed citations breakdown →
16.
Beaulaurier, John, Xue‐Song Zhang, Shijia Zhu, et al.. (2015). Single molecule-level detection and long read-based phasing of epigenetic variations in bacterial methylomes. Nature Communications. 6(1). 74 indexed citations
17.
Topol, Aaron, Jane A. English, Erin Flaherty, et al.. (2015). Increased abundance of translation machinery in stem cell–derived neural progenitor cells from four schizophrenia patients. Translational Psychiatry. 5(10). e662–e662. 43 indexed citations
18.
Chao, Michael C., Shijia Zhu, Satoshi Kimura, et al.. (2015). A Cytosine Methytransferase Modulates the Cell Envelope Stress Response in the Cholera Pathogen. PLoS Genetics. 11(11). e1005666–e1005666. 35 indexed citations
19.
Zhu, Shijia, Guohua Wang, Bo Liu, & Yadong Wang. (2013). Modeling Exon Expression Using Histone Modifications. PLoS ONE. 8(6). e67448–e67448. 10 indexed citations
20.
Zhu, Shijia, et al.. (2001). [Thin basement membrane nephropathy: a mutation in COL4A5 gene].. PubMed. 40(4). 239–42. 3 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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