Junliang Shang

1.6k total citations
127 papers, 1.0k citations indexed

About

Junliang Shang is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Junliang Shang has authored 127 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Molecular Biology, 23 papers in Cancer Research and 19 papers in Genetics. Recurrent topics in Junliang Shang's work include Gene expression and cancer classification (43 papers), Bioinformatics and Genomic Networks (39 papers) and Machine Learning in Bioinformatics (18 papers). Junliang Shang is often cited by papers focused on Gene expression and cancer classification (43 papers), Bioinformatics and Genomic Networks (39 papers) and Machine Learning in Bioinformatics (18 papers). Junliang Shang collaborates with scholars based in China, Taiwan and South Korea. Junliang Shang's co-authors include Jin‐Xing Liu, Chun-Hou Zheng, Ying-Lian Gao, Shasha Yuan, Ling-Yun Dai, Junying Zhang, Juan Wang, Yan Sun, Shengjun Li and Feng Li and has published in prestigious journals such as Bioinformatics, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Junliang Shang

107 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junliang Shang China 20 525 257 168 130 122 127 1.0k
Paola Bertolazzi Italy 17 404 0.8× 67 0.3× 152 0.9× 113 0.9× 155 1.3× 54 981
Genevera I. Allen United States 23 500 1.0× 92 0.4× 310 1.8× 92 0.7× 137 1.1× 62 1.3k
Shikui Tu China 21 1.2k 2.3× 96 0.4× 103 0.6× 145 1.1× 41 0.3× 87 1.7k
Colin Molter Japan 12 563 1.1× 42 0.2× 280 1.7× 115 0.9× 198 1.6× 28 1.1k
Yen‐Jen Oyang Taiwan 16 296 0.6× 53 0.2× 216 1.3× 31 0.2× 84 0.7× 71 984
Alioune Ngom Canada 18 540 1.0× 36 0.1× 255 1.5× 69 0.5× 40 0.3× 90 1.2k
Patrick E. Meyer Belgium 12 954 1.8× 118 0.5× 196 1.2× 52 0.4× 20 0.2× 18 1.3k
Hongwei Chen China 19 845 1.6× 75 0.3× 237 1.4× 100 0.8× 58 0.5× 49 1.4k
Lei Du China 16 387 0.7× 125 0.5× 131 0.8× 17 0.1× 213 1.7× 61 869

Countries citing papers authored by Junliang Shang

Since Specialization
Citations

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

Fields of papers citing papers by Junliang Shang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junliang Shang

This figure shows the co-authorship network connecting the top 25 collaborators of Junliang Shang. A scholar is included among the top collaborators of Junliang Shang 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 Junliang Shang. Junliang Shang 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.
Shang, Junliang, et al.. (2025). RPMVCDA: Random Perturbation and Multi-View Graph Convolutional Networks for CircRNA-Disease Association Prediction. PubMed. 22(1). 192–202. 1 indexed citations
2.
Li, Yanfen, Lujuan Dang, Hanxiang Wang, et al.. (2025). Transformer-based detection of abnormal rice growth using drone-based multispectral imaging. Computers and Electronics in Agriculture. 239. 111055–111055.
3.
Shang, Junliang, et al.. (2024). BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data. IEEE Journal of Biomedical and Health Informatics. 29(1). 495–506. 1 indexed citations
4.
Shang, Junliang, et al.. (2024). A review: simulation tools for genome-wide interaction studies. Briefings in Functional Genomics. 23(6). 745–753. 1 indexed citations
5.
Wang, Linping, et al.. (2023). KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification. Computational Biology and Chemistry. 104. 107862–107862. 1 indexed citations
6.
Li, Feng, et al.. (2023). A framework for scRNA-seq data clustering based on multi-view feature integration. Biomedical Signal Processing and Control. 89. 105785–105785. 4 indexed citations
7.
Shang, Junliang, Xuhui Zhu, Yan Sun, et al.. (2023). DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer. BMC Bioinformatics. 24(1). 13–13. 4 indexed citations
8.
Shang, Junliang, et al.. (2023). GCCN: Graph Capsule Convolutional Network for Progressive Mild Cognitive Impairment Prediction and Pathogenesis Identification Based on Imaging Genetic Data. IEEE Journal of Biomedical and Health Informatics. 27(6). 2968–2979. 9 indexed citations
9.
Li, Feng, et al.. (2023). scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising. Interdisciplinary Sciences Computational Life Sciences. 15(4). 590–601. 1 indexed citations
10.
Li, Feng, et al.. (2023). MGCNRF: Prediction of Disease-Related miRNAs Based on Multiple Graph Convolutional Networks and Random Forest. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 15701–15709. 8 indexed citations
11.
Li, Feng, Jin‐Xing Liu, Junliang Shang, et al.. (2022). NESM: a network embedding method for tumor stratification by integrating multi-omics data. G3 Genes Genomes Genetics. 12(11). 4 indexed citations
12.
Li, Feng, et al.. (2022). NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction. International Journal of Molecular Sciences. 23(17). 9838–9838. 13 indexed citations
13.
Liu, Jin‐Xing, et al.. (2022). MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(1). 534–543. 16 indexed citations
14.
Tuo, Shouheng, Chao Li, Fan Liu, et al.. (2022). MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions. Complex & Intelligent Systems. 9(1). 637–658. 17 indexed citations
15.
Gao, Ying-Lian, et al.. (2022). Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases. Future Generation Computer Systems. 134. 247–255. 7 indexed citations
16.
Shang, Junliang, et al.. (2021). A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(1). 52–60. 1 indexed citations
17.
Zhang, Yuanyuan, et al.. (2020). SINE: Second-Order Information Network Embedding. IEEE Access. 8. 139044–139051. 3 indexed citations
18.
Geng, Zengchao, et al.. (2016). 化学改性提高木质素水溶性及其对Zn 2+ 的络合能力. 35(11). 2216–2223.
19.
Wang, Yaxuan, Jin‐Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, & Junliang Shang. (2016). Differentially expressed genes selection via Laplacian regularized low-rank representation method. Computational Biology and Chemistry. 65. 185–192. 15 indexed citations
20.
Shang, Junliang, et al.. (2016). 施用生物炭对 土微生物量碳、氮及酶活性的影响. 49(6). 1142–1151.

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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