Yu Shi

2.9k total citations · 1 hit paper
49 papers, 1.5k citations indexed

About

Yu Shi is a scholar working on Molecular Biology, Artificial Intelligence and Human-Computer Interaction. According to data from OpenAlex, Yu Shi has authored 49 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 20 papers in Artificial Intelligence and 13 papers in Human-Computer Interaction. Recurrent topics in Yu Shi's work include Speech and dialogue systems (11 papers), Gaze Tracking and Assistive Technology (8 papers) and Bioinformatics and Genomic Networks (7 papers). Yu Shi is often cited by papers focused on Speech and dialogue systems (11 papers), Gaze Tracking and Assistive Technology (8 papers) and Bioinformatics and Genomic Networks (7 papers). Yu Shi collaborates with scholars based in United States, Australia and China. Yu Shi's co-authors include Fang Chen, Ronnie Taib, Eric H. C. Choi, Natalie Ruiz, Brygida Bisikirska, Chan Aye Thu, Donald Petrey, Lei Deng, Li Qiang and Tom Maniatis and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Yu Shi

48 papers receiving 1.4k citations

Hit Papers

Structure-based prediction of protein–protein interaction... 2012 2026 2016 2021 2012 100 200 300 400 500
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. Structure-based prediction of protein–protein interactions on a genome-wide scale
    2012 527 citations Qiangfeng Cliff Zhang, Donald Petrey et al. Nature profile →

Peers

Yu Shi
Steve Pettifer United Kingdom
Pengyu Hong United States
Lucian Ilie Canada
Jingyi Jessica Li United States
Janice Glasgow Canada
Seonwoo Min South Korea
Thin Nguyen Australia
Philippe Bonnet Denmark
Forrest Sheng Bao United States
Xiaolin Xu China
Steve Pettifer United Kingdom
Yu Shi
Citations per year, relative to Yu Shi Yu Shi (= 1×) peers Steve Pettifer

Countries citing papers authored by Yu Shi

Since Specialization
Citations

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

Fields of papers citing papers by Yu Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Shi. A scholar is included among the top collaborators of Yu Shi 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 Yu Shi. Yu Shi 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.
Shi, Yu, Zhiyuan Liu, & Chenyan Xiong. (2025). Craw4LLM: Efficient Web Crawling for LLM Pretraining. 13843–13851.
2.
Chen, Yong, et al.. (2024). Fully Distributed Prescribed-Time Optimization With Time-Varying Cost Function. IEEE Transactions on Vehicular Technology. 74(3). 3627–3637. 2 indexed citations
3.
Zhao, Chengsong, et al.. (2021). Detailed characterization of the UMAMIT proteins provides insight into their evolution, amino acid transport properties, and role in the plant. Journal of Experimental Botany. 72(18). 6400–6417. 24 indexed citations
4.
Setty, Shaun P., David A. Liem, Yu Shi, et al.. (2019). Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications. Journal of Visualized Experiments. 3 indexed citations
5.
Shi, Yu, Huan Gui, Qi Zhu, Lance Kaplan, & Jiawei Han. (2018). AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks. Society for Industrial and Applied Mathematics eBooks. 2018. 144–152. 62 indexed citations
6.
Guerra, Damian D., Réjane Pratelli, Yu Shi, et al.. (2017). Control of Amino Acid Homeostasis by a Ubiquitin Ligase-Coactivator Protein Complex. Journal of Biological Chemistry. 292(9). 3827–3840. 6 indexed citations
7.
Cheng, Wei, Yu Shi, Xiang Zhang, & Wei Wang. (2016). Sparse regression models for unraveling group and individual associations in eQTL mapping. BMC Bioinformatics. 17(1). 136–136. 4 indexed citations
8.
Cheng, Wei, Yu Shi, Xiang Zhang, & Wei Wang. (2015). Fast and robust group-wise eQTL mapping using sparse graphical models. BMC Bioinformatics. 16(1). 2–2. 12 indexed citations
9.
Shi, Yu, Réjane Pratelli, Cynthia J. Denbow, & Guillaume Pilot. (2015). Suppressor mutations in the Glutamine Dumper1 protein dissociate disturbance in amino acid transport from other characteristics of the Gdu1D phenotype. Frontiers in Plant Science. 6. 593–593. 8 indexed citations
10.
11.
Dong, Hui, et al.. (2013). Analysis of genetic differentiation and gene flow among different geographic populations of Oedaleus infernalis (Orthoptera: Acrididae) based on mtDNA COI gene sequences.. Acta Entomologica Sinica. 56(8). 907–916. 1 indexed citations
12.
Wu, Mengyun, Dao‐Qing Dai, Yu Shi, Hong Yan, & Xiao-Fei Zhang. (2012). Biomarker Identification and Cancer Classification Based on Microarray Data Using Laplace Naive Bayes Model with Mean Shrinkage. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(6). 1649–1662. 34 indexed citations
13.
Zhang, Qiangfeng Cliff, Donald Petrey, Lei Deng, et al.. (2012). Structure-based prediction of protein–protein interactions on a genome-wide scale. Nature. 490(7421). 556–560. 527 indexed citations breakdown →
14.
Hildreth, Sherry B., Haibing Yang, Kim Harich, et al.. (2011). Tobacco nicotine uptake permease (NUP1) affects alkaloid metabolism. Proceedings of the National Academy of Sciences. 108(44). 18179–18184. 93 indexed citations
15.
Sun, Yong, Yu Shi, Fang Chen, & Yuk Ying Chung. (2009). Skipping spare information in multimodal inputs during multimodal input fusion. 451–456. 3 indexed citations
16.
Qu, Guiqin, Xiang Liu, Yali Zhang, et al.. (2009). Evidence for programmed cell death and activation of specific caspase-like enzymes in the tomato fruit heat stress response. Planta. 229(6). 1269–1279. 48 indexed citations
17.
Shi, Yu, et al.. (2008). Developing a smart camera for road traffic surveillance. 826–831. 3 indexed citations
18.
Ruiz, Natalie, Ronnie Taib, Yu Shi, Eric H. C. Choi, & Fang Chen. (2007). Using pen input features as indices of cognitive load. 315–318. 27 indexed citations
19.
Shi, Yu, Ronnie Taib, Natalie Ruiz, Eric H. C. Choi, & Fang Chen. (2007). MULTIMODAL HUMAN-MACHINE INTERFACE AND USER COGNITIVE LOAD MEASUREMENT. IFAC Proceedings Volumes. 40(16). 200–205. 4 indexed citations
20.
Yu, Zhengquan, Qingyong Meng, Huanhuan Yu, et al.. (2006). Expression and Bioactivity of Recombinant Human Lysozyme in the Milk of Transgenic Mice. Journal of Dairy Science. 89(8). 2911–2918. 32 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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