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
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
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- Cancer-related molecular mechanisms research
Papers in
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- RNA and protein synthesis mechanisms 2
- Bioinformatics and Genomic Networks 1
- Machine Learning in Bioinformatics 1
- RNA Research and Splicing 1
- Gene expression and cancer classification 1
- Biomedical Text Mining and Ontologies 1
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- Topic Modeling 1
- Co-authors
- David A. Hendrix (3 shared papers)Reza Ghaeini (1 shared paper)Liang Huang (1 shared paper)Steven T. Hill (1 shared paper)Rachael Kuintzle (1 shared paper)Phillip Wallis (1 shared paper)
- Journals
- Nucleic Acids Research (2 papers)The Florida AI Research Society (1 paper)PubMed (1 paper)
- Partner nations
- United States
In The Last Decade
Padideh Danaee
5 papers receiving 369 citations
Peers
Comparison fields: 5 of 58
- Molecular Biology 290
- Cancer Research 61
- Health Informatics 5
- Artificial Intelligence 106
- 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 | 191 | |
| 2 | 2018 | 129 | |
| 3 | 2018 | 60 | |
| 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 382 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (2 papers), Bioinformatics and Genomic Networks (1 paper), Machine Learning in Bioinformatics (1 paper), RNA Research and Splicing (1 paper), Cancer-related molecular mechanisms research (1 paper), Gene expression and cancer classification (1 paper), Topic Modeling (1 paper) and Biomedical Text Mining and Ontologies (1 paper). The work is most often cited by research in Molecular Biology (290 citations), Cancer Research (61 citations), Health Informatics (5 citations), Artificial Intelligence (106 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, Steven T. Hill, Rachael Kuintzle and Phillip Wallis. Their work appears in journals such as Nucleic Acids Research, The Florida AI Research Society and PubMed.
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.