Cao Xiao
- Artificial Intelligence top 0.5%
- Molecular Biology top 5%
- Computational Theory and Mathematics top 0.5%
- Health Information Management top 0.1%
- Materials Chemistry top 10%
- Topics
- Machine Learning in Healthcare (38 papers)Computational Drug Discovery Methods (21 papers)Topic Modeling (18 papers)
- Journals
- Nature CommunicationsJournal of Clinical OncologySHILAP Revista de lepidopterología
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Cao Xiao
109 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 169
- Artificial Intelligence 1.9k
- Molecular Biology 1.3k
- Computational Theory and Mathematics 1.1k
- Health Information Management 628
- Materials Chemistry 440
Countries citing papers authored by Cao Xiao
This map shows the geographic impact of Cao Xiao'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 Cao Xiao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cao Xiao more than expected).
Fields of papers citing papers by Cao Xiao
This network shows the impact of papers produced by Cao Xiao. 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 Cao Xiao. The network helps show where Cao Xiao may publish in the future.
Co-authorship network of co-authors of Cao Xiao
This figure shows the co-authorship network connecting the top 25 collaborators of Cao Xiao. A scholar is included among the top collaborators of Cao Xiao 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 Cao Xiao. Cao Xiao 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 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 5 | |
| 7 | 17 | |
| 8 | 19 | |
| 9 | 90 | |
| 10 | 1 | |
| 11 | 4 | |
| 12 | Patient-Trial Matching with Deep Embedding and Entailment Prediction. | 1 |
| 13 | Clinical Report Auto-completion. | 1 |
| 14 | DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening | 12 |
| 15 | SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules | 1 |
| 16 | EEGtoText: Learning to Write Medical Reports from EEG Recordings. | 3 |
| 17 | 30 | |
| 18 | MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare | 46 |
| 19 | 74 | |
| 20 | Development of and application demulsifier for crude oil | 1 |
About Cao Xiao
Cao Xiao is a scholar working on Computational Mathematics, Health Information Management and Health Informatics, having authored 122 papers that have together received 4.1k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (38 papers), Computational Drug Discovery Methods (21 papers) and Topic Modeling (18 papers). The work is most often cited by research in Health Information Management (628 citations), Health Informatics (165 citations) and Computational Theory and Mathematics (1.1k citations). Cao Xiao has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jimeng Sun, Lucas M. Glass, Kexin Huang, Edward Choi, Jiayu Zhou, Fei Wang, Tengfei Ma, Junyuan Shang, Tianfan Fu and Marinka Žitnik. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology 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.