Lezheng Yu

1.4k total citations · 1 hit paper
25 papers, 1.1k citations indexed

About

Lezheng Yu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Lezheng Yu has authored 25 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 2 papers in Genetics. Recurrent topics in Lezheng Yu's work include Machine Learning in Bioinformatics (21 papers), Genomics and Phylogenetic Studies (13 papers) and RNA and protein synthesis mechanisms (10 papers). Lezheng Yu is often cited by papers focused on Machine Learning in Bioinformatics (21 papers), Genomics and Phylogenetic Studies (13 papers) and RNA and protein synthesis mechanisms (10 papers). Lezheng Yu collaborates with scholars based in China and Poland. Lezheng Yu's co-authors include Menglong Li, Yanzhi Guo, Zhining Wen, Jiesi Luo, Yizhou Li, Li V. Yang, Yuhong Zeng, Rong-quan Xiao, Runyu Jing and Fengjuan Liu and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Frontiers in Microbiology.

In The Last Decade

Lezheng Yu

24 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

2008 523 citations Yanzhi Guo, Lezheng Yu et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lezheng Yu China	12	1.1k	253	37	33	23	25	1.1k
Weiliang Zhu China	5	733 0.7×	216 0.9×	25 0.7×	9 0.3×	12 0.5×	8	776
Jerico Revote Australia	13	827 0.8×	95 0.4×	57 1.5×	29 0.9×	26 1.1×	20	963
Noelia Ferruz Spain	12	652 0.6×	117 0.5×	24 0.6×	40 1.2×	59 2.6×	20	818
Shengli Zhang China	19	878 0.8×	105 0.4×	51 1.4×	19 0.6×	51 2.2×	68	970
Raphaël A. G. Chaleil United Kingdom	11	757 0.7×	247 1.0×	12 0.3×	42 1.3×	36 1.6×	19	1.0k
Gyu Rie Lee South Korea	14	748 0.7×	122 0.5×	18 0.5×	40 1.2×	10 0.4×	23	909
Dan Ofer Israel	10	697 0.6×	110 0.4×	48 1.3×	33 1.0×	95 4.1×	17	881
Tom Gibbs United States	2	929 0.9×	188 0.7×	63 1.7×	40 1.2×	61 2.7×	4	1.1k
Minh N. Nguyen Singapore	13	558 0.5×	68 0.3×	29 0.8×	23 0.7×	11 0.5×	31	712
Ghalia Rehawi Germany	3	928 0.9×	188 0.7×	63 1.7×	40 1.2×	61 2.7×	4	1.1k

Countries citing papers authored by Lezheng Yu

Since Specialization

Citations

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

Fields of papers citing papers by Lezheng Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lezheng Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Lezheng Yu. A scholar is included among the top collaborators of Lezheng Yu 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 Lezheng Yu. Lezheng Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Yu, Lezheng, et al.. (2025). Multimodal deep learning for allergenic proteins prediction. BMC Biology. 23(1). 232–232.

Zhang, Yonglin, Lezheng Yu, Li C. Xue, et al.. (2025). Optimizing lipocalin sequence classification with ensemble deep learning models. PLoS ONE. 20(4). e0319329–e0319329. 1 indexed citations

Yu, Lezheng, Yonglin Zhang, Xue Li, et al.. (2023). EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework. Computational and Structural Biotechnology Journal. 21. 4836–4848. 4 indexed citations

Zhang, Yonglin, Lezheng Yu, Runyu Jing, Bin Han, & Jiesi Luo. (2023). Fast and Efficient Design of Deep Neural Networks for Predicting N⁷-Methylguanosine Sites Using autoBioSeqpy. ACS Omega. 8(22). 19728–19740. 4 indexed citations

Yu, Lezheng, Yonglin Zhang, Xue Li, et al.. (2023). Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy. Frontiers in Microbiology. 14. 1175925–1175925. 6 indexed citations

Jing, Runyu, Li C. Xue, Menglong Li, Lezheng Yu, & Jiesi Luo. (2022). layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP. iScience. 25(12). 105530–105530. 10 indexed citations

Yu, Lezheng, Xue Li, Fengjuan Liu, et al.. (2022). The applications of deep learning algorithms on in silico druggable proteins identification. Journal of Advanced Research. 41. 219–231. 33 indexed citations

Yu, Lezheng, Yonglin Zhang, Xue Li, et al.. (2022). Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning. Frontiers in Microbiology. 13. 843425–843425. 11 indexed citations

Yu, Lezheng, Fengjuan Liu, Yizhou Li, Jiesi Luo, & Runyu Jing. (2021). DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors. Frontiers in Microbiology. 12. 605782–605782. 11 indexed citations

10.

Jing, Runyu, et al.. (2021). DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework. NAR Genomics and Bioinformatics. 3(4). lqab086–lqab086. 12 indexed citations

11.

Guo, Jiali, Ziyan Huang, Xingyong Zhu, et al.. (2019). A method for gene essentiality in miRNA-TF-mRNA co-regulatory network and its application on prostate cancer. Chemometrics and Intelligent Laboratory Systems. 190. 33–40. 1 indexed citations

12.

Yu, Lezheng, et al.. (2013). In silico identification of Gram-negative bacterial secreted proteins from primary sequence. Computers in Biology and Medicine. 43(9). 1177–1181. 14 indexed citations

13.

Li, Yizhou, Juan Li, Lezheng Yu, et al.. (2011). Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes. Computational Biology and Chemistry. 36. 31–35. 7 indexed citations

14.

Li, Yizhou, Zhining Wen, Hui Yin, et al.. (2011). Predicting disease-associated substitution of a single amino acid by analyzing residue interactions. BMC Bioinformatics. 12(1). 14–14. 39 indexed citations

15.

Yu, Lezheng, Yanzhi Guo, Zheng Zhang, et al.. (2010). SecretP: A new method for predicting mammalian secreted proteins. Peptides. 31(4). 574–578. 32 indexed citations

16.

Yu, Lezheng, et al.. (2010). SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition. Journal of Theoretical Biology. 267(1). 1–6. 109 indexed citations

17.

Wu, Jiang, Menglong Li, Lezheng Yu, & Chao Wang. (2010). An Ensemble Classifier of Support Vector Machines Used to Predict Protein Structural Classes by Fusing Auto Covariance and Pseudo-Amino Acid Composition. The Protein Journal. 29(1). 62–67. 23 indexed citations

18.

Zeng, Yuhong, Yanzhi Guo, Rong-quan Xiao, et al.. (2009). Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. Journal of Theoretical Biology. 259(2). 366–372. 183 indexed citations

19.

Wu, Jiang, et al.. (2009). Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance. Interdisciplinary Sciences Computational Life Sciences. 1(4). 315–319. 4 indexed citations

20.

Guo, Yanzhi, Lezheng Yu, Zhining Wen, & Menglong Li. (2008). Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences. Nucleic Acids Research. 36(9). 3025–3030. 523 indexed citations breakdown →

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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