C. Wolfe
Impact in
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- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
- Genetics, Bioinformatics, and Biomedical Research
Papers in
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- Advanced Neural Network Applications 4
- Graph Theory and Algorithms 1
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- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 2
- Stochastic Gradient Optimization Techniques 2
- Co-authors
- Anastasios Kyrillidis (5 shared papers)Chen Dun (3 shared papers)Mohammadamin Edrisi (1 shared paper)CJ Barberan (1 shared paper)Nicolae Sapoval (1 shared paper)Ruth Dannenfelser (1 shared paper)Luay Nakhleh (1 shared paper)R. A. Leo Elworth (1 shared paper)
- Journals
- Machine Learning (1 paper)Nature Communications (1 paper)Proceedings of the VLDB Endowment (1 paper)ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesSingapore
In The Last Decade
C. Wolfe
6 papers receiving 201 citations
C. Wolfe's Hit Papers
Peers
Comparison fields: 5 of 76
- Health Informatics 5
- Molecular Biology 124
- Artificial Intelligence 52
- Biophysics 6
- Computational Theory and Mathematics 16
Countries citing papers authored by C. Wolfe
This map shows the geographic impact of C. Wolfe'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 C. Wolfe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Wolfe more than expected).
Fields of papers citing papers by C. Wolfe
This network shows the impact of papers produced by C. Wolfe. 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 C. Wolfe. The network helps show where C. Wolfe may publish in the future.
Co-authors
The 25 scholars most cited alongside C. Wolfe, 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 | Current progress and open challenges for applying deep learning across the biosciences Hit paper breakdown → | 2022 | 171 |
| 2 | 2022 | 18 | |
| 3 | 2022 | 10 | |
| 4 | 2023 | 5 | |
| 5 | 2019 | 2 | |
| 6 | Data Augmentation for Deep Transfer Learning. | 2019 | 1 |
| 7 | 2024 | 1 |
About C. Wolfe
C. Wolfe is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Computational Mechanics and Computational Theory and Mathematics, having authored 7 papers that have together received 208 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Cell Image Analysis Techniques (1 paper), COVID-19 diagnosis using AI (1 paper) and Graph Theory and Algorithms (1 paper). The work is most often cited by research in Health Informatics (5 citations), Molecular Biology (124 citations), Artificial Intelligence (52 citations), Biophysics (6 citations) and Computational Theory and Mathematics (16 citations). C. Wolfe has collaborated with scholars based in United States and Singapore. Frequent co-authors include Anastasios Kyrillidis, Chen Dun, Mohammadamin Edrisi, CJ Barberan, Nicolae Sapoval, Ruth Dannenfelser, Luay Nakhleh, R. A. Leo Elworth, Zhi Yan and Richard G. Baraniuk. Their work appears in journals such as Machine Learning, Nature Communications, Proceedings of the VLDB Endowment, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) and arXiv (Cornell University).
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.