Ghada Badr
- Molecular Biology
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Science Applications top 10%
- Information Systems
- Co-authors
- Yousef Al-OhaliHala AlshamlanB. John OommenAreej Al‐WabilHassan MathkourKrister M. SwensonIsra Al-TuraikiDavid Sankoff
- Topics
- Algorithms and Data Compression (7 papers)Gene expression and cancer classification (6 papers)Machine Learning in Bioinformatics (4 papers)
- Journals
- BMC BioinformaticsBioMed Research InternationalIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
- Partner nations
- CanadaSaudi ArabiaEgypt
In The Last Decade
Ghada Badr
19 papers receiving 433 citations
Peers
Comparison fields: 5 of 75
- Molecular Biology 287
- Artificial Intelligence 251
- Computer Vision and Pattern Recognition 67
- Computer Science Applications 46
- Information Systems 27
Countries citing papers authored by Ghada Badr
This map shows the geographic impact of Ghada Badr'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 Ghada Badr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ghada Badr more than expected).
Fields of papers citing papers by Ghada Badr
This network shows the impact of papers produced by Ghada Badr. 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 Ghada Badr. The network helps show where Ghada Badr may publish in the future.
Co-authorship network of co-authors of Ghada Badr
This figure shows the co-authorship network connecting the top 25 collaborators of Ghada Badr. A scholar is included among the top collaborators of Ghada Badr 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 Ghada Badr. Ghada Badr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 40 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 48 | |
| 5 | 6 | |
| 6 | 155 | |
| 7 | 164 | |
| 8 | 1 | |
| 9 | A Review of Effective Gene Selection Methods for Cancer Classification Using Microarray Gene Expression Profile | 2 |
| 10 | 1 | |
| 11 | 15 | |
| 12 | 2 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 5 | |
| 16 | 6 | |
| 17 | 5 | |
| 18 | 3 | |
| 19 | 1 |
About Ghada Badr
Ghada Badr is a scholar working on Artificial Intelligence, Health Information Management and Computer Science Applications, having authored 19 papers that have together received 458 indexed citations. Recurring topics across this work include Algorithms and Data Compression (7 papers), Gene expression and cancer classification (6 papers) and Machine Learning in Bioinformatics (4 papers). The work is most often cited by research in Computer Science Applications (46 citations), Artificial Intelligence (251 citations) and Molecular Biology (287 citations). Ghada Badr has collaborated with scholars based in Canada, Saudi Arabia and Egypt. Frequent co-authors include Yousef Al-Ohali, Hala Alshamlan, B. John Oommen, Areej Al‐Wabil, Hassan Mathkour, Krister M. Swenson, Isra Al-Turaiki, David Sankoff, David Sankoff and Marcel Turcotte. Their work appears in journals such as BMC Bioinformatics, BioMed Research International and IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics).
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.