Leilei Yan

741 total citations
28 papers, 582 citations indexed

About

Leilei Yan is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Leilei Yan has authored 28 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Artificial Intelligence. Recurrent topics in Leilei Yan's work include Cancer-related gene regulation (7 papers), Epigenetics and DNA Methylation (5 papers) and Cancer-related molecular mechanisms research (4 papers). Leilei Yan is often cited by papers focused on Cancer-related gene regulation (7 papers), Epigenetics and DNA Methylation (5 papers) and Cancer-related molecular mechanisms research (4 papers). Leilei Yan collaborates with scholars based in China, United States and Taiwan. Leilei Yan's co-authors include Y. George Zheng, Zhiming Jin, Xuebing Yan, Zezhi Shan, Yuan Tian, Sihong Liu, Li Zhang, Chao Yang, You Feng and Xinyang Zhao and has published in prestigious journals such as Journal of the American Chemical Society, Molecular Cell and Journal of Medicinal Chemistry.

In The Last Decade

Leilei Yan

25 papers receiving 578 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leilei Yan China	11	414	98	73	56	38	28	582
Clement Chung United States	14	515 1.2×	168 1.7×	82 1.1×	17 0.3×	28 0.7×	49	955
Rong Ma United States	12	180 0.4×	81 0.8×	59 0.8×	29 0.5×	66 1.7×	34	499
Naga Rajiv Lakkaniga United States	15	234 0.6×	106 1.1×	30 0.4×	153 2.7×	22 0.6×	27	586
Amanda C. Schierz United Kingdom	8	532 1.3×	198 2.0×	125 1.7×	21 0.4×	10 0.3×	11	739
Jong-Kwang Kim South Korea	12	288 0.7×	61 0.6×	144 2.0×	18 0.3×	9 0.2×	39	590
Matthew P. Humphries United Kingdom	16	174 0.4×	243 2.5×	119 1.6×	37 0.7×	16 0.4×	30	575
Christhunesa S. Christudass United States	13	422 1.0×	104 1.1×	103 1.4×	23 0.4×	44 1.2×	28	795
K. Pavan Kalyan India	4	201 0.5×	186 1.9×	52 0.7×	39 0.7×	9 0.2×	5	449
Yuehua Li China	10	349 0.8×	181 1.8×	205 2.8×	28 0.5×	39 1.0×	36	663
Eléonore Gravier France	8	275 0.7×	172 1.8×	130 1.8×	12 0.2×	18 0.5×	9	437

Countries citing papers authored by Leilei Yan

Since Specialization

Citations

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

Fields of papers citing papers by Leilei Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leilei Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Leilei Yan. A scholar is included among the top collaborators of Leilei Yan 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 Leilei Yan. Leilei Yan 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

Yan, Leilei, et al.. (2025). Group equivariant learning for few-shot image classification. Applied Intelligence. 55(10).

Yan, Leilei, et al.. (2025). Contrastive prototype loss based discriminative feature network for few-shot learning. Applied Intelligence. 55(5).

Li, Gang, et al.. (2024). CartoonDiff: Training-free Cartoon Image Generation with Diffusion Transformer Models. 3825–3829. 7 indexed citations

Yan, Leilei, et al.. (2023). Discriminant space metric network for few-shot image classification. Applied Intelligence. 53(14). 17444–17459. 9 indexed citations

Yan, Leilei, et al.. (2023). Deep feature network with multi-scale fusion for highly congested crowd counting. International Journal of Machine Learning and Cybernetics. 15(3). 819–835. 3 indexed citations

Yan, Lin, Mingkun Yang, Li Huang, et al.. (2023). A bacteria-derived tetramerized protein ameliorates nonalcoholic steatohepatitis in mice via binding and relocating acetyl-coA carboxylase. Cell Reports. 42(11). 113453–113453. 1 indexed citations

Zhang, Li, et al.. (2022). Sparse discriminant twin support vector machine for binary classification. Neural Computing and Applications. 34(19). 16173–16198. 5 indexed citations

Zhang, Li, et al.. (2021). CTSVM: A robust twin support vector machine with correntropy-induced loss function for binary classification problems. Information Sciences. 559. 22–45. 19 indexed citations

Zhang, Li, et al.. (2021). T$$^2$$CNN: a novel method for crowd counting via two-task convolutional neural network. The Visual Computer. 39(1). 73–85. 7 indexed citations

10.

Yan, Leilei, et al.. (2021). Deeper multi-column dilated convolutional network for congested crowd understanding. Neural Computing and Applications. 34(2). 1407–1422. 8 indexed citations

11.

Yan, Leilei & Li Zhang. (2019). Unsupervised Dimension Reduction Using Supervised Orthogonal Discriminant Projection for Clustering. 2239–2246. 1 indexed citations

12.

Yang, Fang, et al.. (2019). Catheter Aspiration With Recanalization for Budd-Chiari Syndrome With Inferior Vena Cava Thrombosis. Surgical Laparoscopy Endoscopy & Percutaneous Techniques. 29(4). 304–307. 7 indexed citations

13.

Liu, Liguo, Xuebing Yan, Zezhi Shan, et al.. (2017). Anorectal functional outcome following laparoscopic low anterior resection for rectal cancer. Molecular and Clinical Oncology. 6(4). 613–621. 9 indexed citations

14.

Yan, Xuebing, Zezhi Shan, Leilei Yan, et al.. (2016). High expression of Zinc-finger protein X-linked promotes tumor growth and predicts a poor outcome for stage II/III colorectal cancer patients. Oncotarget. 7(15). 19680–19692. 23 indexed citations

15.

Yan, Xuebing, Leilei Yan, Sihong Liu, et al.. (2015). N-cadherin, a novel prognostic biomarker, drives malignant progression of colorectal cancer. Molecular Medicine Reports. 12(2). 2999–3006. 77 indexed citations

16.

Li, Yin, Xuebing Yan, Leilei Yan, et al.. (2015). High expression of Zinc-finger protein X-linked is associated with reduced E-cadherin expression and unfavorable prognosis in nasopharyngeal carcinoma.. PubMed. 8(4). 3919–27. 8 indexed citations

17.

Shan, Zezhi, Xuebing Yan, Leilei Yan, et al.. (2015). Overexpression of Tbx3 is correlated with Epithelial-Mesenchymal Transition phenotype and predicts poor prognosis of colorectal cancer.. PubMed. 5(1). 344–53. 33 indexed citations

18.

Yan, Xuebing, Leilei Yan, Qingchao Zhu, et al.. (2014). Zinc-finger protein X-linked is a novel predictor of prognosis in patients with colorectal cancer.. PubMed. 7(6). 3150–7. 18 indexed citations

19.

Xu, Jian, Juan A. Osés-Prieto, Kalpana Makhijani, et al.. (2013). Arginine Methylation Initiates BMP-Induced Smad Signaling. Molecular Cell. 51(1). 5–19. 87 indexed citations

20.

Wang, Juxian, Limin Chen, Sarmistha Halder Sinha, et al.. (2012). Pharmacophore-Based Virtual Screening and Biological Evaluation of Small Molecule Inhibitors for Protein Arginine Methylation. Journal of Medicinal Chemistry. 55(18). 7978–7987. 61 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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