Mark Junjie Li

1.1k total citations
26 papers, 596 citations indexed

About

Mark Junjie Li is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mark Junjie Li has authored 26 papers receiving a total of 596 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 8 papers in Molecular Biology and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mark Junjie Li's work include Advanced Clustering Algorithms Research (5 papers), Gene expression and cancer classification (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Mark Junjie Li is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Gene expression and cancer classification (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Mark Junjie Li collaborates with scholars based in China, United States and Hong Kong. Mark Junjie Li's co-authors include Michael K. Ng, Joshua Zhexue Huang, Yiu‐ming Cheung, Jian Huang, Zengyou He, Nguyễn Thị Thanh Thủy, Qingyao Wu, Thanh-Tung Nguyen, Thanh-Tung Nguyen and Yunming Ye and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Cell Metabolism and Cancer Research.

In The Last Decade

Mark Junjie Li

21 papers receiving 577 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Junjie Li China 11 301 148 108 89 79 26 596
Da Zhang China 13 107 0.4× 77 0.5× 99 0.9× 77 0.9× 69 0.9× 49 648
Rui Tang China 15 328 1.1× 79 0.5× 60 0.6× 30 0.3× 87 1.1× 44 675
Andri Mirzal Malaysia 7 243 0.8× 178 1.2× 235 2.2× 45 0.5× 50 0.6× 23 653
A. Fazel Famili Canada 10 219 0.7× 58 0.4× 132 1.2× 40 0.4× 97 1.2× 22 625
Hong Peng China 15 255 0.8× 246 1.7× 141 1.3× 74 0.8× 59 0.7× 55 678
Ricardo Cerri Brazil 16 494 1.6× 148 1.0× 225 2.1× 30 0.3× 120 1.5× 69 844
Saúl Solorio-Fernández Mexico 7 366 1.2× 214 1.4× 82 0.8× 39 0.4× 69 0.9× 8 662
Wei Zhong United States 9 170 0.6× 37 0.3× 94 0.9× 98 1.1× 50 0.6× 34 414
Zhiwei Lin United Kingdom 12 285 0.9× 129 0.9× 124 1.1× 29 0.3× 65 0.8× 41 627

Countries citing papers authored by Mark Junjie Li

Since Specialization
Citations

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

Fields of papers citing papers by Mark Junjie Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Junjie Li. 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 Mark Junjie Li. The network helps show where Mark Junjie Li may publish in the future.

Co-authorship network of co-authors of Mark Junjie Li

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Junjie Li. A scholar is included among the top collaborators of Mark Junjie Li 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 Mark Junjie Li. Mark Junjie Li 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.
Li, Mark Junjie, Gen Zhao, Qin Zhang, et al.. (2025). Anomaly detection in attributed networks via local multi-order contrastive learning and global topology awareness. Neurocomputing. 650. 130829–130829.
2.
Wang, Xu, et al.. (2024). Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-Labeling. IEEE Transactions on Multimedia. 26. 11164–11175. 1 indexed citations
3.
Qian, Qingwen, Mark Junjie Li, Zeyuan Zhang, et al.. (2024). Obesity disrupts the pituitary-hepatic UPR communication leading to NAFLD progression. Cell Metabolism. 36(7). 1550–1565.e9. 12 indexed citations
4.
Li, Mark Junjie, et al.. (2024). Tracing Feature Tests to Textual Requirements. 120–125. 3 indexed citations
5.
Li, Mark Junjie, et al.. (2023). An Interval RSP-based ensemble model for big data analysis. 2023. 16–23. 1 indexed citations
6.
Huang, Wenxi, et al.. (2023). Multi-modal Chinese Fake News Detection. 109–117.
7.
Xiao, Chuan, et al.. (2021). HSGAN: Reducing mode collapse in GANs by the latent code distance of homogeneous samples. Computer Vision and Image Understanding. 214. 103314–103314. 12 indexed citations
8.
Yamoah, Ebenezer N., Mark Junjie Li, Karen L. Elliott, et al.. (2020). Using Sox2 to alleviate the hallmarks of age-related hearing loss. Ageing Research Reviews. 59. 101042–101042. 28 indexed citations
9.
Azhar, Muhammad, et al.. (2019). A hierarchical Gamma Mixture Model-based method for estimating the number of clusters in complex data. Applied Soft Computing. 87. 105891–105891. 10 indexed citations
10.
Azhar, Muhammad, Mark Junjie Li, & Joshua Zhexue Huang. (2019). A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data. Entropy. 21(9). 906–906. 3 indexed citations
11.
Lightner, Amy L., Zeji Du, Timothy E. Peterson, et al.. (2019). Commonly Used Immunosuppressives Affect Mesenchymal Stem Cell Viability and Function: Should We Rethinking Clinical Trial Inclusion and Exclusion Criteria?. Crohn s & Colitis 360. 1(3). 1 indexed citations
12.
Singh, Raman Deep, Matthew L. Hillestad, Christopher Livia, et al.. (2018). M 3 RNA Drives Targeted Gene Delivery in Acute Myocardial Infarction. Tissue Engineering Part A. 25(1-2). 145–158. 19 indexed citations
13.
Li, Mark Junjie, et al.. (2018). Grey relational analysis using Gaussian process regression method for dissolved gas concentration prediction. International Journal of Machine Learning and Cybernetics. 10(6). 1313–1322. 18 indexed citations
14.
Chmielecki, Juliann, Zhongwu Lai, Kristy Potts, et al.. (2018). Abstract 5582: Use of circulating tumor DNA (ctDNA) to predict superiority of dosing schedules in early clinical development. Cancer Research. 78(13_Supplement). 5582–5582.
15.
Nguyen, Thanh-Tung, Joshua Zhexue Huang, Qingyao Wu, Nguyễn Thị Thanh Thủy, & Mark Junjie Li. (2015). Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests. BMC Genomics. 16(S2). S5–S5. 75 indexed citations
16.
Nguyen, Thanh-Tung, et al.. (2015). Forecasting Time Series Water Levels on Mekong River Using Machine Learning Models. 292–297. 24 indexed citations
17.
Xiao, Yong, et al.. (2014). Application of Wavelet-based clustering approach to load profiling on AMI measurements. 1537–1540. 8 indexed citations
18.
Liu, Yang, Mark Junjie Li, Yiu‐ming Cheung, Pak C. Sham, & Michael K. Ng. (2009). SKM-SNP: SNP markers detection method. Journal of Biomedical Informatics. 43(2). 233–239. 6 indexed citations
19.
Li, Mark Junjie, Michael K. Ng, Yiu‐ming Cheung, & Jian Huang. (2008). Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters. IEEE Transactions on Knowledge and Data Engineering. 20(11). 1519–1534. 202 indexed citations
20.
Ng, Michael K., Mark Junjie Li, Joshua Zhexue Huang, & Zengyou He. (2007). On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(3). 503–507. 147 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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