Padideh Danaee
Impact in
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- RNA and protein synthesis mechanisms
- RNA modifications and cancer
- Gene expression and cancer classification
- RNA Research and Splicing
- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
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- Cancer-related molecular mechanisms research
Papers in
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- RNA and protein synthesis mechanisms 2
- Machine Learning in Bioinformatics 1
- Bioinformatics and Genomic Networks 1
- Biomedical Text Mining and Ontologies 1
- Gene expression and cancer classification 1
- RNA modifications and cancer 1
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- AI in cancer detection 1
- Machine Learning in Healthcare 1
- Co-authors
- David A. Hendrix (3 shared papers)Reza Ghaeini (1 shared paper)Liang Huang (1 shared paper)Rachael Kuintzle (1 shared paper)Steven T. Hill (1 shared paper)Phillip Wallis (1 shared paper)
- Journals
- Nucleic Acids Research (2 papers)PubMed (1 paper)The Florida AI Research Society (1 paper)
- Partner nations
- United States
In The Last Decade
Padideh Danaee
5 papers receiving 385 citations
Peers
Comparison fields: 5 of 57
- Molecular Biology 279
- Cancer Research 56
- Health Informatics 5
- Artificial Intelligence 99
- Health Information Management 12
Countries citing papers authored by Padideh Danaee
This map shows the geographic impact of Padideh Danaee'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 Padideh Danaee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Padideh Danaee more than expected).
Fields of papers citing papers by Padideh Danaee
This network shows the impact of papers produced by Padideh Danaee. 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 Padideh Danaee. The network helps show where Padideh Danaee may publish in the future.
Co-authors
The 6 scholars most cited alongside Padideh Danaee, 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 | 2016 | 193 | |
| 2 | 2018 | 140 | |
| 3 | 2018 | 62 | |
| 4 | Learning Semantic Relationships from Medical Codes. | 2019 | 1 |
| 5 | Interpretable Machine Learning: Applications in Biology and Genomics | 2019 | 1 |
About Padideh Danaee
Padideh Danaee is a scholar working on Molecular Biology, Artificial Intelligence, Cancer Research, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 397 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (2 papers), Machine Learning in Bioinformatics (1 paper), Bioinformatics and Genomic Networks (1 paper), AI in cancer detection (1 paper), Biomedical Text Mining and Ontologies (1 paper), Gene expression and cancer classification (1 paper), RNA modifications and cancer (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Molecular Biology (279 citations), Cancer Research (56 citations), Health Informatics (5 citations), Artificial Intelligence (99 citations) and Health Information Management (12 citations). Padideh Danaee has collaborated with scholars based in United States. Frequent co-authors include David A. Hendrix, Reza Ghaeini, Liang Huang, Rachael Kuintzle, Steven T. Hill and Phillip Wallis. Their work appears in journals such as Nucleic Acids Research, PubMed and The Florida AI Research Society.
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.