Xuefeng Cui

761 total citations
40 papers, 428 citations indexed

About

Xuefeng Cui is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Xuefeng Cui has authored 40 papers receiving a total of 428 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 11 papers in Computational Theory and Mathematics and 8 papers in Materials Chemistry. Recurrent topics in Xuefeng Cui's work include Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (11 papers) and Machine Learning in Bioinformatics (9 papers). Xuefeng Cui is often cited by papers focused on Protein Structure and Dynamics (13 papers), Computational Drug Discovery Methods (11 papers) and Machine Learning in Bioinformatics (9 papers). Xuefeng Cui collaborates with scholars based in China, Saudi Arabia and United States. Xuefeng Cui's co-authors include Xin Gao, Cheng Chen, Hiroyuki Kuwahara, Bin Yu, Meshari Alazmi, Adil Salhi, Sheng Wang, Zhiwu Lu, Shi Han and Jim Jing-Yan Wang and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and The Journal of Physical Chemistry B.

In The Last Decade

Xuefeng Cui

39 papers receiving 424 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Xuefeng Cui 294 110 50 45 35 40 428
David La 571 1.9× 131 1.2× 169 3.4× 24 0.5× 25 0.7× 19 720
Khushboo Bafna 455 1.5× 83 0.8× 100 2.0× 36 0.8× 15 0.4× 17 627
Wenzhi Mao 330 1.1× 75 0.7× 74 1.5× 45 1.0× 27 0.8× 11 441
Jorge Roel‐Touris 528 1.8× 123 1.1× 86 1.7× 35 0.8× 31 0.9× 19 697
Charles Christoffer 469 1.6× 147 1.3× 182 3.6× 24 0.5× 29 0.8× 25 543
Naomi K. Fox 766 2.6× 98 0.9× 226 4.5× 39 0.9× 35 1.0× 9 855
Xinqiang Ding 363 1.2× 66 0.6× 70 1.4× 19 0.4× 9 0.3× 19 444
Stefan Seemayer 399 1.4× 50 0.5× 107 2.1× 50 1.1× 21 0.6× 5 429
Jinrui Xu 652 2.2× 86 0.8× 176 3.5× 88 2.0× 39 1.1× 18 817
Subu Subramanian 394 1.3× 62 0.6× 48 1.0× 40 0.9× 22 0.6× 8 542

Countries citing papers authored by Xuefeng Cui

Since Specialization
Citations

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

Fields of papers citing papers by Xuefeng Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xuefeng Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Xuefeng Cui. A scholar is included among the top collaborators of Xuefeng Cui 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 Xuefeng Cui. Xuefeng Cui 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.
Zha, Yuguo, et al.. (2024). Comprehensive profiling of antibiotic resistance genes in diverse environments and novel function discovery. 2(1). 100054–100054. 6 indexed citations
2.
Liu, Yanmin, et al.. (2024). How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?. 817–822. 1 indexed citations
3.
Wang, Yuxiao, et al.. (2024). Exploring Trial-and-Error in Deep Learning: Initial Application to Isotope Detection in Mass Spectrometry. Big Data Mining and Analytics. 7(4). 1251–1261. 1 indexed citations
4.
5.
Gao, Jing, Kai Hu, Fa Zhang, & Xuefeng Cui. (2023). Hexagonal image segmentation on spatially resolved transcriptomics. Methods. 220. 61–68. 2 indexed citations
6.
Zhao, Wei, et al.. (2023). Prediction of protein-ligand binding affinity with deep learning. Computational and Structural Biotechnology Journal. 21. 5796–5806. 12 indexed citations
7.
Gao, Jing, Fa Zhang, Kai Hu, & Xuefeng Cui. (2022). Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 200–205. 3 indexed citations
8.
Hao, Yu, Xiaohua Wan, Rui Yan, et al.. (2022). VP-Detector: A 3D multi-scale dense convolutional neural network for macromolecule localization and classification in cryo-electron tomograms. Computer Methods and Programs in Biomedicine. 221. 106871–106871. 7 indexed citations
9.
Zha, Yuguo, Hui Chong, H. Qiu, et al.. (2022). Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches. Genome Medicine. 14(1). 43–43. 14 indexed citations
10.
Zhang, Xuan, et al.. (2022). Boosting Deep Learning-based Docking with Cross-attention and Centrality Embedding. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2 indexed citations
11.
Chen, Cheng, et al.. (2022). Pretraining Transformers for TCR-pMHC Binding Prediction. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 26–31. 2 indexed citations
12.
Chen, Cheng, Shi Han, Zhiwen Jiang, et al.. (2021). DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Computers in Biology and Medicine. 136. 104676–104676. 72 indexed citations
13.
Wang, Lusheng, et al.. (2021). SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data. Journal of Computational Biology. 28(8). 774–788. 3 indexed citations
14.
Yang, Pengshuo, et al.. (2020). Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology. NAR Genomics and Bioinformatics. 2(2). lqaa042–lqaa042. 4 indexed citations
15.
Chaillet, Marten L., Gijs van der Schot, Remco C. Veltkamp, et al.. (2020). SHREC 2020: Classification in cryo-electron tomograms. Computers & Graphics. 91. 279–289. 29 indexed citations
16.
Zhu, Lizhe, Fu Kit Sheong, Xuefeng Cui, et al.. (2016). Understanding the core of RNA interference: The dynamic aspects of Argonaute-mediated processes. Progress in Biophysics and Molecular Biology. 128. 39–46. 7 indexed citations
17.
Kuwahara, Hiroyuki, Meshari Alazmi, Xuefeng Cui, & Xin Gao. (2016). MRE: a web tool to suggest foreign enzymes for the biosynthesis pathway design with competing endogenous reactions in mind. Nucleic Acids Research. 44(W1). W217–W225. 43 indexed citations
18.
Wang, Jim Jing-Yan, Ivor W. Tsang, Xuefeng Cui, Zhiwu Lu, & Xin Gao. (2016). Multi-instance dictionary learning via multivariate performance measure optimization. Pattern Recognition. 66. 448–459. 1 indexed citations
19.
Cui, Xuefeng, Hammad Naveed, & Xin Gao. (2015). Finding optimal interaction interface alignments between biological complexes. Bioinformatics. 31(12). i133–i141. 12 indexed citations
20.
Cui, Xuefeng, Shuai Cheng Li, Dongbo Bu, Babak Alipanahi, & Ming Li. (2013). Protein Structure Idealization: How accurately is it possible to model protein structures with dihedral angles?. Algorithms for Molecular Biology. 8(1). 5–5. 2 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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