Chi Zhou

1.8k total citations · 1 hit paper
36 papers, 1.2k citations indexed

About

Chi Zhou is a scholar working on Molecular Biology, Artificial Intelligence and Immunology. According to data from OpenAlex, Chi Zhou has authored 36 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 9 papers in Artificial Intelligence and 8 papers in Immunology. Recurrent topics in Chi Zhou's work include CRISPR and Genetic Engineering (7 papers), Evolutionary Algorithms and Applications (7 papers) and vaccines and immunoinformatics approaches (6 papers). Chi Zhou is often cited by papers focused on CRISPR and Genetic Engineering (7 papers), Evolutionary Algorithms and Applications (7 papers) and vaccines and immunoinformatics approaches (6 papers). Chi Zhou collaborates with scholars based in China, United States and United Kingdom. Chi Zhou's co-authors include Qi Liu, Peter Nelson, Weimin Xiao, Chenyu Zhu, Thomas M. Tirpak, Guohui Chuai, Zhiting Wei, Hanhui Ma, Bin Duan and Sheng Qu and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and Journal of Hazardous Materials.

In The Last Decade

Chi Zhou

32 papers receiving 1.1k citations

Hit Papers

DeepCRISPR: optimized CRISPR guide RNA design by deep lea... 2018 2026 2020 2023 2018 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. DeepCRISPR: optimized CRISPR guide RNA design by deep learning
    2018 356 citations Guohui Chuai, Hanhui Ma et al. Genome biology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chi Zhou China 16 843 225 214 162 100 36 1.2k
Stefan Horn Germany 20 617 0.7× 66 0.3× 123 0.6× 194 1.2× 103 1.0× 53 1.2k
Jae Woo Choi South Korea 19 996 1.2× 51 0.2× 136 0.6× 314 1.9× 58 0.6× 34 1.6k
Rui Yin China 21 417 0.5× 253 1.1× 78 0.4× 24 0.1× 58 0.6× 82 1.1k
Gráinne Kerr Germany 16 832 1.0× 56 0.2× 223 1.0× 195 1.2× 14 0.1× 20 1.2k
Chunguang Ma China 19 325 0.4× 376 1.7× 73 0.3× 91 0.6× 20 0.2× 98 1.2k
Travis S. Johnson United States 16 723 0.9× 167 0.7× 142 0.7× 195 1.2× 116 1.2× 52 1.3k
Paul G. O’Reilly United Kingdom 19 364 0.4× 162 0.7× 87 0.4× 286 1.8× 130 1.3× 51 1.0k
Margaret Guo United States 9 507 0.6× 72 0.3× 177 0.8× 103 0.6× 202 2.0× 15 993

Countries citing papers authored by Chi Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Chi Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chi Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Chi Zhou. A scholar is included among the top collaborators of Chi Zhou 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 Chi Zhou. Chi Zhou 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.
Zhang, Hailong, Qiannan Duan, Jianchao Lee, et al.. (2025). A multi-component heavy metal detection method using UV-Vis superimposed spectrum and deep learning. Journal of Hazardous Materials. 496. 139187–139187.
2.
3.
Gao, Yuli, Siqi Wu, Chi Zhou, et al.. (2025). Weakly supervised peptide-TCR binding prediction facilitates neoantigen identification. Cell Systems. 16(10). 101403–101403.
4.
Zhou, Chi, et al.. (2025). Development and application of an interbacterial DNA delivery system based on M13 bacteriophage. Archives of Microbiology. 207(5). 113–113.
5.
Gao, Yuli, Chengyu Zhu, Zhiting Wei, et al.. (2023). Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition. Nature Machine Intelligence. 5(3). 236–249. 83 indexed citations
6.
Felip, Enriqueta, Chi Zhou, Ihor Vynnychenko, et al.. (2022). 80O Atezolizumab (atezo) vs best supportive care (BSC) in stage II-IIIA NSCLC with high PD-L1 expression: Sub-analysis from the pivotal phase III IMpower010 study. Annals of Oncology. 33. S71–S71. 10 indexed citations
7.
Zhang, Biyu, Chen Tang, Yanli Yao, et al.. (2021). The tumor therapy landscape of synthetic lethality. Nature Communications. 12(1). 1275–1275. 46 indexed citations
8.
Zhou, Chi, et al.. (2020). Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer. Frontiers in Oncology. 10. 593989–593989. 25 indexed citations
9.
Zhou, Chi, Lihua Tang, Yukang Gong, et al.. (2020). ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq. Aging. 12(14). 14633–14648. 32 indexed citations
10.
Wei, Zhiting, Chi Zhou, Ming Guan, et al.. (2019). The Landscape of Tumor Fusion Neoantigens: A Pan-Cancer Analysis. iScience. 21. 249–260. 47 indexed citations
11.
Zhou, Chi, Zhiting Wei, Biyu Zhang, et al.. (2019). pTuneos: prioritizing tumor neoantigens from next-generation sequencing data. Genome Medicine. 11(1). 67–67. 60 indexed citations
12.
Duan, Bin, Chi Zhou, Chengyu Zhu, et al.. (2019). Model-based understanding of single-cell CRISPR screening. Nature Communications. 10(1). 2233–2233. 67 indexed citations
13.
Zhou, Chi, Chenyu Zhu, & Qi Liu. (2019). Toward in silico Identification of Tumor Neoantigens in Immunotherapy. Trends in Molecular Medicine. 25(11). 980–992. 42 indexed citations
14.
Chuai, Guohui, Hanhui Ma, Jifang Yan, et al.. (2018). DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome biology. 19(1). 80–80. 356 indexed citations breakdown →
15.
Yan, Jifang, Guohui Chuai, Qi Tao, et al.. (2017). MetaTopics: an integration tool to analyze microbial community profile by topic model. BMC Genomics. 18(S1). 962–962. 15 indexed citations
16.
Gong, Tingting, et al.. (2014). Validation of a blood group genotyping method based on high-resolution melting curve analysis. Immunohematology. 30(4). 161–165. 5 indexed citations
17.
Sun, Hao, Chi Zhou, Xiaoqin Huang, et al.. (2013). Correlation between the linguistic affinity and genetic diversity of Chinese ethnic groups. Journal of Human Genetics. 58(10). 686–693. 16 indexed citations
18.
Ruan, Feng, et al.. (2009). Research of Association Technology in Assembly Design for Precision Progressive Die. 27(1). 32–36. 2 indexed citations
19.
Li, Xin, Chi Zhou, Weimin Xiao, & Peter Nelson. (2005). Prefix Gene Expression Programming. Genetic and Evolutionary Computation Conference. 37 indexed citations
20.
Zhou, Chi. (2003). Gene expression programming and rule induction for domain knowledge discovery and management.. Figshare. 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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