Cheng Liang
Impact in
- Cancer Research top 2%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Computational Mathematics top 5%
Papers in
-
- Cancer-related molecular mechanisms research 37
- MicroRNA in disease regulation 31
Cheng Liang
88 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 153
- Cancer Research 982
- Computational Mathematics 24
- Computer Graphics and Computer-Aided Design 83
- Molecular Biology 1.5k
- Periodontics 82
Countries citing papers authored by Cheng Liang
This map shows the geographic impact of Cheng Liang'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 Cheng Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cheng Liang more than expected).
Fields of papers citing papers by Cheng Liang
This network shows the impact of papers produced by Cheng Liang. 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 Cheng Liang. The network helps show where Cheng Liang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Cheng Liang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 8 | |
| 7 | 2023 | 20 | |
| 8 | 2023 | 6 | |
| 9 | 2023 | 2 | |
| 10 | 2022 | 6 | |
| 11 | 2020 | 37 | |
| 12 | Long Noncoding RNA LINC00265 Targets EGFR and Promotes Deterioration of Colorectal Cancer: A Comprehensive Study Based on Data Mining and in vitro Validation | 2019 | 1 |
| 13 | 2019 | 22 | |
| 14 | 2019 | 84 | |
| 15 | 2019 | 22 | |
| 16 | 2019 | 9 | |
| 17 | 2018 | 22 | |
| 18 | 2018 | 64 | |
| 19 | Parametric 3D Modeling of Hypoid Gear by Asynchronous Mode Based on Pro/Toolkit | 2012 | 0 |
| 20 | Research and Development of SoftPLC | 2006 | 0 |
About Cheng Liang
Cheng Liang is a scholar working on Computational Mathematics, Cancer Research, Molecular Biology, Computational Theory and Mathematics and Computer Vision and Pattern Recognition, having authored 95 papers that have together received 2.2k indexed citations. Recurring topics across this work include Cancer-related molecular mechanisms research (37 papers), MicroRNA in disease regulation (31 papers), RNA modifications and cancer (17 papers), Circular RNAs in diseases (14 papers), Computational Drug Discovery Methods (12 papers), Machine Learning in Bioinformatics (11 papers), Bioinformatics and Genomic Networks (9 papers) and Face and Expression Recognition (6 papers). The work is most often cited by research in Cancer Research (982 citations), Computational Mathematics (24 citations), Computer Graphics and Computer-Aided Design (83 citations), Molecular Biology (1.5k citations) and Periodontics (82 citations). Cheng Liang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jiawei Luo, Pingjian Ding, Qiu Xiao, Guanghui Li, Jie Cai, Huaxiang Zhang, Junjie Yang, Ka‐Chun Wong, Lin Wang and C.-T. Chen. Their work appears in journals such as IEEE Access, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Briefings in Bioinformatics, Neurocomputing and Expert Systems with Applications.
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.