Cong Shen

509 total citations
26 papers, 350 citations indexed

About

Cong Shen is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Cong Shen has authored 26 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 9 papers in Cancer Research. Recurrent topics in Cong Shen's work include Computational Drug Discovery Methods (11 papers), Bioinformatics and Genomic Networks (7 papers) and MicroRNA in disease regulation (7 papers). Cong Shen is often cited by papers focused on Computational Drug Discovery Methods (11 papers), Bioinformatics and Genomic Networks (7 papers) and MicroRNA in disease regulation (7 papers). Cong Shen collaborates with scholars based in China, Singapore and United States. Cong Shen's co-authors include Jiawei Luo, Xinru Tang, Pingjian Ding, Kelin Xia, M. M. Khonsari, Xiangtao Chen, JunJie Wee, Jie Cai, Ying Liu and Yahui Long and has published in prestigious journals such as ACS Nano, Bioinformatics and PLoS ONE.

In The Last Decade

Cong Shen

23 papers receiving 343 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Cong Shen China	12	243	128	92	33	32	26	350
Shanyi Wang China	9	622 2.6×	28 0.2×	62 0.7×	24 0.7×	34 1.1×	12	721
Xiangyi Li China	10	87 0.4×	9 0.1×	88 1.0×	66 2.0×	51 1.6×	22	326
Xiangeng Wang China	11	325 1.3×	34 0.3×	190 2.1×	75 2.3×	31 1.0×	17	446
Haitao Fu China	6	202 0.8×	35 0.3×	170 1.8×	58 1.8×	36 1.1×	28	313
Ziang Lu China	10	157 0.6×	12 0.1×	33 0.4×	50 1.5×	24 0.8×	24	328
Zhen-Hao Guo China	13	342 1.4×	186 1.5×	124 1.3×	24 0.7×	27 0.8×	26	460
Shaoqi Chen China	11	85 0.3×	16 0.1×	21 0.2×	20 0.6×	51 1.6×	29	287
Krishnan Srinivasan United States	5	179 0.7×	52 0.4×	7 0.1×	11 0.3×	14 0.4×	6	277
Junkai Liu China	8	111 0.5×	4 0.0×	120 1.3×	36 1.1×	34 1.1×	30	278

Countries citing papers authored by Cong Shen

Since Specialization

Citations

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

Fields of papers citing papers by Cong Shen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cong Shen

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shen, Cong, et al.. (2026). Molecular Topological Deep Learning for Polymer Property Prediction. ACS Nano. 20(1). 288–299.

Shen, Cong, Xiang Liu, Jiawei Luo, & Kelin Xia. (2025). Torsion Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(4). 2946–2956.

Shen, Cong, et al.. (2024). Curvature-enhanced graph convolutional network for biomolecular interaction prediction. Computational and Structural Biotechnology Journal. 23. 1016–1025. 8 indexed citations

Jiang, Zhenyu, et al.. (2024). Deep graph contrastive learning model for drug-drug interaction prediction. PLoS ONE. 19(6). e0304798–e0304798. 4 indexed citations

Jiang, Zhenyu, et al.. (2024). Geometric Molecular Graph Representation Learning Model for Drug-Drug Interactions Prediction. IEEE Journal of Biomedical and Health Informatics. 28(12). 7623–7632. 3 indexed citations

Zhang, Li, et al.. (2024). Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Improved HOG Features. Sensors. 24(5). 1590–1590. 11 indexed citations

Zhong, Yichen, et al.. (2023). Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations. Artificial Intelligence in Medicine. 145. 102665–102665. 2 indexed citations

Shen, Cong, et al.. (2023). Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs. iScience. 26(10). 108020–108020. 3 indexed citations

Shen, Cong, Jiawei Luo, & Kelin Xia. (2023). Molecular geometric deep learning. Cell Reports Methods. 3(11). 100621–100621. 17 indexed citations

10.

Luo, Jiawei, et al.. (2023). Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism. Briefings in Bioinformatics. 24(5). 24 indexed citations

11.

Chen, Xiangtao, et al.. (2022). scSAGAN: A scRNA-seq data imputation method based on Semi-Supervised Learning and Probabilistic Latent Semantic Analysis. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 178–181.

12.

Zhong, Yichen, et al.. (2022). Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge. Interdisciplinary Sciences Computational Life Sciences. 14(3). 775–785. 2 indexed citations

13.

Luo, Jiawei, et al.. (2021). Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network. Interdisciplinary Sciences Computational Life Sciences. 13(4). 547–558. 3 indexed citations

14.

Chen, Xin, et al.. (2021). An In Silico Method for Predicting Drug Synergy Based on Multitask Learning. Interdisciplinary Sciences Computational Life Sciences. 13(2). 299–311. 8 indexed citations

15.

Tang, Xinru, et al.. (2021). Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction. Briefings in Bioinformatics. 22(6). 120 indexed citations

16.

Shen, Cong, et al.. (2020). Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge. Journal of Chemical Information and Modeling. 60(8). 4085–4097. 13 indexed citations

17.

Luo, Jiawei, et al.. (2020). Incorporating Clinical, Chemical and Biological Information for Predicting Small Molecule-microRNA Associations Based on Non-Negative Matrix Factorization. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(6). 2535–2545. 15 indexed citations

18.

Li, Donghui, Cong Shen, Jian Luo, et al.. (2019). Research on Data Fusion of Adaptive Weighted Multi-source Sensor. Computers, materials & continua/Computers, materials & continua (Print). 61(3). 1217–1231. 18 indexed citations

19.

Ding, Pingjian, et al.. (2019). Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization. Journal of Chemical Information and Modeling. 60(1). 37–46. 18 indexed citations

20.

Shen, Cong, et al.. (2016). Tribological Performance of Polyamide-Imide Seal Ring Under Seawater Lubrication. Tribology Letters. 62(3). 18 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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