Xiaogen Zhou

3.1k total citations · 1 hit paper
82 papers, 1.9k citations indexed

About

Xiaogen Zhou is a scholar working on Molecular Biology, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, Xiaogen Zhou has authored 82 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 17 papers in Artificial Intelligence and 16 papers in Materials Chemistry. Recurrent topics in Xiaogen Zhou's work include Protein Structure and Dynamics (30 papers), Machine Learning in Bioinformatics (21 papers) and RNA and protein synthesis mechanisms (18 papers). Xiaogen Zhou is often cited by papers focused on Protein Structure and Dynamics (30 papers), Machine Learning in Bioinformatics (21 papers) and RNA and protein synthesis mechanisms (18 papers). Xiaogen Zhou collaborates with scholars based in China, United States and United Kingdom. Xiaogen Zhou's co-authors include Guijun Zhang, Yang Zhang, Wei Zheng, Chengxin Zhang, Eric W. Bell, Yang Li, Robin Pearce, Xiaoqiang Huang, Jun Hu and Dong‐Jun Yu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Xiaogen Zhou

77 papers receiving 1.9k citations

Hit Papers

I-TASSER-MTD: a deep-learning-based platform for multi-do... 2022 2026 2023 2024 2022 50 100 150 200 250
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. I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction
    2022 297 citations Xiaogen Zhou, Wei Zheng et al. Nature Protocols profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaogen Zhou China 24 1.1k 264 262 205 186 82 1.9k
Jianzhu Ma United States 27 2.2k 2.0× 489 1.9× 337 1.3× 309 1.5× 94 0.5× 69 3.1k
Arvind Ramanathan United States 26 967 0.8× 185 0.7× 260 1.0× 180 0.9× 80 0.4× 130 1.8k
Yutaka Akiyama Japan 30 1.5k 1.3× 387 1.5× 309 1.2× 143 0.7× 387 2.1× 206 3.5k
Zhenfeng Zhang China 33 879 0.8× 95 0.4× 550 2.1× 225 1.1× 281 1.5× 96 2.7k
Haiguang Liu China 23 930 0.8× 154 0.6× 457 1.7× 122 0.6× 234 1.3× 119 1.9k
Harinder Singh United States 25 1.8k 1.6× 338 1.3× 64 0.2× 75 0.4× 198 1.1× 66 2.9k
Jan A. Hiss Switzerland 28 2.5k 2.2× 709 2.7× 345 1.3× 104 0.5× 61 0.3× 62 3.9k
Francisco S. Domingues Germany 26 2.1k 1.9× 339 1.3× 452 1.7× 67 0.3× 390 2.1× 75 3.1k
Lorenz Wernisch United Kingdom 30 1.8k 1.6× 146 0.6× 170 0.6× 138 0.7× 554 3.0× 81 2.9k
Chongli Qin United Kingdom 6 1.5k 1.3× 398 1.5× 523 2.0× 349 1.7× 71 0.4× 7 2.4k

Countries citing papers authored by Xiaogen Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Xiaogen Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaogen Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaogen Zhou. A scholar is included among the top collaborators of Xiaogen 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 Xiaogen Zhou. Xiaogen 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
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Zheng, Wei, Qiqige Wuyun, Yang Li, et al.. (2025). Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER. Nature Biotechnology. 18 indexed citations
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Zhou, Xiaogen, Tong Tong, Zhixiong Zhong, Haoyi Fan, & Zuoyong Li. (2023). Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation. Computers in Biology and Medicine. 154. 106551–106551. 13 indexed citations
7.
Wang, Jianchao, Min Du, Hejun Zhang, et al.. (2023). Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer. Cell Reports Medicine. 4(4). 101004–101004. 7 indexed citations
8.
Zhou, Xiaogen, Wei Zheng, Yang Li, et al.. (2022). I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction. Nature Protocols. 17(10). 2326–2353. 297 indexed citations breakdown →
9.
Liu, Jun, et al.. (2022). DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning. Bioinformatics. 38(7). 1895–1903. 30 indexed citations
10.
Zhou, Xiaogen, et al.. (2022). Structural analogue-based protein structure domain assembly assisted by deep learning. Bioinformatics. 38(19). 4513–4521. 17 indexed citations
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Zhou, Xiaogen, et al.. (2022). BSG-Net: A Blind Super-resolution Guided Network for Improving Ultrasound Image Segmentation. 38 2. 172–179. 1 indexed citations
12.
Liu, Jun, et al.. (2021). MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction. Bioinformatics. 37(23). 4350–4356. 23 indexed citations
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Xia, Yuhao, et al.. (2021). A sequential niche multimodal conformational sampling algorithm for protein structure prediction. Bioinformatics. 37(23). 4357–4365. 8 indexed citations
14.
Liu, Jun, et al.. (2021). Distance-guided protein folding based on generalized descent direction. Briefings in Bioinformatics. 22(6). 4 indexed citations
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Li, Yang, Chengxin Zhang, Eric W. Bell, et al.. (2021). Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLoS Computational Biology. 17(3). e1008865–e1008865. 59 indexed citations
17.
Zheng, Wei, Yang Li, Chengxin Zhang, et al.. (2021). Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14. Proteins Structure Function and Bioinformatics. 89(12). 1734–1751. 44 indexed citations
18.
Zhang, Chengxin, Wei Zheng, Xiaoqiang Huang, et al.. (2020). Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1. Journal of Proteome Research. 19(4). 1351–1360. 202 indexed citations
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Antoniadis, D.A., et al.. (2016). A Hybrid Process Design Kit: Towards Integrating CMOS and III-V Devices. TechConnect Briefs. 4(2016). 313–318. 1 indexed citations
20.
Tseng, Jennifer, B L Kemp, Fadlo R. Khuri, et al.. (1999). Loss of Fhit is frequent in stage I non-small cell lung cancer and in the lungs of chronic smokers.. PubMed. 59(19). 4798–803. 80 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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