Tianfan Fu
- Computational Theory and Mathematics top 1%
- Molecular Biology
- Artificial Intelligence top 5%
- Materials Chemistry
- Signal Processing top 5%
- Topics
- Computational Drug Discovery Methods (11 papers)Machine Learning in Materials Science (10 papers)Machine Learning in Healthcare (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsNature Chemical Biology
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Tianfan Fu
32 papers receiving 789 citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Computational Theory and Mathematics 380
- Molecular Biology 368
- Artificial Intelligence 251
- Materials Chemistry 198
- Signal Processing 145
Countries citing papers authored by Tianfan Fu
This map shows the geographic impact of Tianfan Fu'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 Tianfan Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tianfan Fu more than expected).
Fields of papers citing papers by Tianfan Fu
This network shows the impact of papers produced by Tianfan Fu. 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 Tianfan Fu. The network helps show where Tianfan Fu may publish in the future.
Co-authorship network of co-authors of Tianfan Fu
This figure shows the co-authorship network connecting the top 25 collaborators of Tianfan Fu. A scholar is included among the top collaborators of Tianfan Fu 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 Tianfan Fu. Tianfan Fu 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 | 0 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 4 | |
| 7 | 0 | |
| 8 | 6 | |
| 9 | 9 | |
| 10 | 24 | |
| 11 | 90 | |
| 12 | 27 | |
| 13 | 0 | |
| 14 | 27 | |
| 15 | DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening | 12 |
| 16 | DeepPurpose: a deep learning library for drug–target interaction predictionbreakdown → | 300 |
| 17 | CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC | 8 |
| 18 | Quasi-newton Hamiltonian Monte Carlo | 4 |
| 19 | 113 | |
| 20 | 3 |
About Tianfan Fu
Tianfan Fu is a scholar working on Health Informatics, Computational Theory and Mathematics and Health Information Management, having authored 37 papers that have together received 812 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), Machine Learning in Materials Science (10 papers) and Machine Learning in Healthcare (7 papers). The work is most often cited by research in Computational Theory and Mathematics (380 citations), Health Informatics (27 citations) and Signal Processing (145 citations). Tianfan Fu has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jimeng Sun, Cao Xiao, Lucas M. Glass, Kexin Huang, Marinka Žitnik, Yanmin Qian, Kai Yu, Ya Zhang, Nanxin Chen and Yuan Liu. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and Nature Chemical Biology.
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.