Ibrahim Emam
Impact in
- Molecular Biology top 10%
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Genomics and Phylogenetic Studies
- Biomedical Text Mining and Ontologies
- Molecular Biology Techniques and Applications
- RNA modifications and cancer
- Gene Regulatory Network Analysis
Papers in
-
- Gene expression and cancer classification 8
- Bioinformatics and Genomic Networks 7
- Biomedical Text Mining and Ontologies 3
- Genomics and Phylogenetic Studies 1
-
- Scientific Computing and Data Management 2
- Co-authors
- JAMES M. MALONE (1 shared paper)Misha Kapushesky (1 shared paper)Eleanor M. Williams (1 shared paper)Helen E. Parkinson (1 shared paper)Alvis Brāzma (1 shared paper)Gabriella Rustici (1 shared paper)Ele Holloway (1 shared paper)Shicai Wang (3 shared papers)
- Journals
- Nucleic Acids Research (4 papers)PLoS ONE (1 paper)BMC Genomics (1 paper)BMC Bioinformatics (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- United KingdomGermanyChina
In The Last Decade
Ibrahim Emam
10 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 102
- Molecular Biology 844
- Cancer Research 107
- Information Systems and Management 37
- Aging 9
- Genetics 126
Countries citing papers authored by Ibrahim Emam
This map shows the geographic impact of Ibrahim Emam'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 Ibrahim Emam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ibrahim Emam more than expected).
Fields of papers citing papers by Ibrahim Emam
This network shows the impact of papers produced by Ibrahim Emam. 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 Ibrahim Emam. The network helps show where Ibrahim Emam may publish in the future.
Co-authors
The 19 scholars most cited alongside Ibrahim Emam, 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 | 2008 | 343 | |
| 2 | 2012 | 305 | |
| 3 | 2010 | 284 | |
| 4 | 2009 | 196 | |
| 5 | 2014 | 50 | |
| 6 | 2014 | 18 | |
| 7 | Feature Selection for Cancer Classification: An SVM based Approach | 2012 | 6 |
| 8 | 2006 | 2 | |
| 9 | 2014 | 2 | |
| 10 | 2012 | 2 | |
| 11 | 2018 | 0 |
About Ibrahim Emam
Ibrahim Emam is a scholar working on Molecular Biology, Information Systems and Management, Health Information Management, Computational Theory and Mathematics and Artificial Intelligence, having authored 11 papers that have together received 1.2k indexed citations. Recurring topics across this work include Gene expression and cancer classification (8 papers), Bioinformatics and Genomic Networks (7 papers), Biomedical Text Mining and Ontologies (3 papers), Scientific Computing and Data Management (2 papers), Computational Drug Discovery Methods (1 paper), Genomics and Phylogenetic Studies (1 paper), Artificial Intelligence in Healthcare (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Molecular Biology (844 citations), Cancer Research (107 citations), Information Systems and Management (37 citations), Aging (9 citations) and Genetics (126 citations). Ibrahim Emam has collaborated with scholars based in United Kingdom, Germany and China. Frequent co-authors include JAMES M. MALONE, Misha Kapushesky, Eleanor M. Williams, Helen E. Parkinson, Alvis Brāzma, Gabriella Rustici, Ele Holloway, Shicai Wang, Florian Guitton and Yike Guo. Their work appears in journals such as Nucleic Acids Research, PLoS ONE, BMC Genomics, BMC Bioinformatics and Lecture notes in computer science.
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.