Chao Shen
- Computational Theory and Mathematics top 0.2%
- Molecular Biology top 5%
- Materials Chemistry top 10%
- Pharmacology top 5%
- Organic Chemistry top 10%
- Topics
- Computational Drug Discovery Methods (39 papers)Protein Structure and Dynamics (26 papers)Machine Learning in Materials Science (14 papers)
- Journals
- Nucleic Acids ResearchNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Chao Shen
78 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Computational Theory and Mathematics 1.4k
- Molecular Biology 1.4k
- Materials Chemistry 619
- Pharmacology 185
- Organic Chemistry 159
Countries citing papers authored by Chao Shen
This map shows the geographic impact of Chao 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 Chao Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chao Shen more than expected).
Fields of papers citing papers by Chao Shen
This network shows the impact of papers produced by Chao 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 Chao Shen. The network helps show where Chao Shen may publish in the future.
Co-authorship network of co-authors of Chao Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Chao Shen. A scholar is included among the top collaborators of Chao 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 Chao Shen. Chao Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | 29 | |
| 8 | 80 | |
| 9 | 44 | |
| 10 | 5 | |
| 11 | 20 | |
| 12 | 8 | |
| 13 | 35 | |
| 14 | Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based modelsbreakdown → | 388 |
| 15 | 9 | |
| 16 | Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG | 8 |
| 17 | 11 | |
| 18 | 43 | |
| 19 | Can a heart rate variability biomarker identify the presence of autism spectrum disorder in eight year old children | 3 |
| 20 | 粒子群最適化(PSO)に基づくバックプロパゲーションニューラルネットワーク(BPNN)を用いた車内騒音の音質予測と数理モデル | 1 |
About Chao Shen
Chao Shen is a scholar working on Computational Theory and Mathematics, Molecular Biology and Architecture, having authored 81 papers that have together received 2.3k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (39 papers), Protein Structure and Dynamics (26 papers) and Machine Learning in Materials Science (14 papers). The work is most often cited by research in Computational Theory and Mathematics (1.4k citations), Molecular Biology (1.4k citations) and Materials Chemistry (619 citations). Chao Shen has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Tingjun Hou, Dongsheng Cao, Zhe Wang, Chang‐Yu Hsieh, Zhenhua Wu, Lei Xu, Dejun Jiang, Yu Kang, Xujun Zhang and Ben Liao. Their work appears in journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.
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.