Lingling Zhao
- Molecular Biology
- Automotive Engineering top 5%
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Xiaohong SuJunjie WangPeijun MaChunyu WangShuai WangMichael PechtLong PangLiang Cheng
- Topics
- Computational Drug Discovery Methods (11 papers)Bioinformatics and Genomic Networks (8 papers)Machine Learning in Bioinformatics (7 papers)
- Cited by
- Automotive EngineeringSafety, Risk, Reliability and QualityComputational Theory and Mathematics
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Lingling Zhao
53 papers receiving 862 citations
Peers
Comparison fields: 5 of 109
- Molecular Biology 321
- Automotive Engineering 202
- Computational Theory and Mathematics 176
- Electrical and Electronic Engineering 156
- Computer Vision and Pattern Recognition 142
Countries citing papers authored by Lingling Zhao
This map shows the geographic impact of Lingling Zhao'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 Lingling Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lingling Zhao more than expected).
Fields of papers citing papers by Lingling Zhao
This network shows the impact of papers produced by Lingling Zhao. 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 Lingling Zhao. The network helps show where Lingling Zhao may publish in the future.
Co-authorship network of co-authors of Lingling Zhao
This figure shows the co-authorship network connecting the top 25 collaborators of Lingling Zhao. A scholar is included among the top collaborators of Lingling Zhao 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 Lingling Zhao. Lingling Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 9 | |
| 4 | 5 | |
| 5 | 23 | |
| 6 | 27 | |
| 7 | 16 | |
| 8 | 31 | |
| 9 | 0 | |
| 10 | 23 | |
| 11 | 85 | |
| 12 | 34 | |
| 13 | 2 | |
| 14 | 17 | |
| 15 | 107 | |
| 16 | 3 | |
| 17 | 7 | |
| 18 | 13 | |
| 19 | 2 | |
| 20 | 6 |
About Lingling Zhao
Lingling Zhao is a scholar working on Safety, Risk, Reliability and Quality, Computer Science Applications and Computational Theory and Mathematics, having authored 57 papers that have together received 884 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), Bioinformatics and Genomic Networks (8 papers) and Machine Learning in Bioinformatics (7 papers). The work is most often cited by research in Automotive Engineering (202 citations), Safety, Risk, Reliability and Quality (117 citations) and Computational Theory and Mathematics (176 citations). Lingling Zhao has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Xiaohong Su, Junjie Wang, Peijun Ma, Chunyu Wang, Shuai Wang, Michael Pecht, Long Pang, Liang Cheng, Jun Zhang and Yang Liu. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and International Journal of Molecular Sciences.
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.