Ling Gao

462 total citations
29 papers, 298 citations indexed

About

Ling Gao is a scholar working on Atmospheric Science, Global and Planetary Change and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ling Gao has authored 29 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Atmospheric Science, 7 papers in Global and Planetary Change and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ling Gao's work include Atmospheric chemistry and aerosols (7 papers), Atmospheric Ozone and Climate (6 papers) and Atmospheric and Environmental Gas Dynamics (6 papers). Ling Gao is often cited by papers focused on Atmospheric chemistry and aerosols (7 papers), Atmospheric Ozone and Climate (6 papers) and Atmospheric and Environmental Gas Dynamics (6 papers). Ling Gao collaborates with scholars based in China, Thailand and Japan. Ling Gao's co-authors include Nan Sheng, Ping Xuan, Tiangang Zhang, Toshiya Nakaguchi, Zhongli Ding, Zihua Tang, Junliang Ji, Hanchao Jiang, Lan Huang and Yan Wang and has published in prestigious journals such as Scientific Reports, Atmospheric Environment and Expert Systems with Applications.

In The Last Decade

Ling Gao

29 papers receiving 285 citations

Peers

Ling Gao

GPC

Álvaro Luiz Fazenda Brazil

F. Diblen Netherlands

Sourabh Bhattacharya India

Lenneke M. Jong Australia

Reinert Korsnes Norway

B. Lewis United States

Rémy Thibaud France

Jinxue Wang United States

Shashank Subramanian United States

Álvaro Luiz Fazenda Brazil

Ling Gao

157 ×1.6

29 ×0.4

162 ×2.3

GPC

26 ×0.5

4 ×0.1

Citations per year, relative to Ling Gao Ling Gao (= 1×) peers Álvaro Luiz Fazenda

Countries citing papers authored by Ling Gao

Since Specialization

Citations

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

Fields of papers citing papers by Ling Gao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Gao

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

Liu, Yiming, et al.. (2025). DGJA: Dependency Graph-enhanced Joint Attention Structure for Multimodal Sarcasm Detection. 1–5. 1 indexed citations

Panyametheekul, Sirima, et al.. (2025). Machine learning-based quantification and separation of emissions and meteorological effects on PM2.5 in Greater Bangkok. Scientific Reports. 15(1). 14775–14775. 2 indexed citations

Wang, Ying, et al.. (2025). Unraveling DINCH – Induced hepatotoxicity mechanisms via network toxicology and molecular docking with experimental validation. Ecotoxicology and Environmental Safety. 299. 118305–118305. 1 indexed citations

Gao, Ling, et al.. (2025). TCIP: Network with topology capture and incongruity perception for sarcasm detection. Information Fusion. 117. 102918–102918. 1 indexed citations

Panyametheekul, Sirima, et al.. (2024). Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach. Environmental Science and Pollution Research. 32(59). 31162–31179. 3 indexed citations

Huang, Lan, Nan Sheng, Ling Gao, et al.. (2024). Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases. IEEE Journal of Biomedical and Health Informatics. 29(1). 657–668. 5 indexed citations

Sheng, Nan, Yan Wang, Lan Huang, et al.. (2024). A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(3). 328–347. 12 indexed citations

Gao, Ling, et al.. (2024). First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning. Environmental Research Letters. 19(2). 24040–24040. 4 indexed citations

Gao, Ling, et al.. (2024). PGCL: Prompt guidance and self-supervised contrastive learning-based method for Visual Question Answering. Expert Systems with Applications. 251. 124011–124011. 1 indexed citations

10.

He, Zhonghua, et al.. (2024). Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China. Ecological Indicators. 160. 111946–111946. 8 indexed citations

11.

He, Zhonghua, et al.. (2024). Spatio-temporal modeling of satellite-observed CO2 columns in China using deep learning. International Journal of Applied Earth Observation and Geoinformation. 129. 103859–103859. 6 indexed citations

12.

Huang, Chunlin, Hongrong Shi, Dazhi Yang, et al.. (2023). Retrieval of sub-kilometer resolution solar irradiance from Fengyun-4A satellite using a region-adapted Heliosat-2 method. Solar Energy. 264. 112038–112038. 21 indexed citations

13.

Gao, Ling, et al.. (2023). Longitudinal associations among student–student relationship, moral disengagement, and adolescents’ bullying perpetration.. School Psychology. 38(5). 337–347. 5 indexed citations

14.

Xuan, Ping, et al.. (2022). Heterogeneous multi-scale neighbor topologies enhanced drug–disease association prediction. Briefings in Bioinformatics. 23(3). 2 indexed citations

15.

Gao, Ling, Hui Cui, Tiangang Zhang, Nan Sheng, & Ping Xuan. (2021). Prediction of drug–disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. Briefings in Bioinformatics. 23(1). 10 indexed citations

16.

Xuan, Ping, Ling Gao, Nan Sheng, Tiangang Zhang, & Toshiya Nakaguchi. (2020). Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations. IEEE Journal of Biomedical and Health Informatics. 25(5). 1793–1804. 48 indexed citations

17.

Gao, Ling, et al.. (2020). Multi-Level Joint Feature Learning for Person Re-Identification. Algorithms. 13(5). 111–111. 5 indexed citations

18.

Gao, Ling, et al.. (2020). Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification. Algorithms. 13(8). 193–193. 1 indexed citations

19.

Gao, Ling. (2015). The study of gravity-magnetic anomaly and tectonic background in Sichuan west region. 10 indexed citations

20.

Gao, Ling. (2003). Design and development of Taxokeys, a dichotomous-reasoning-based multimedia expert system assisting insect identification and taxonomic study. Acta Entomologica Sinica. 1 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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