M Ibrahim
Impact in
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- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Metabolomics and Mass Spectrometry Studies
- Gene Regulatory Network Analysis
- Machine Learning in Bioinformatics
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- Computational Drug Discovery Methods
Papers in
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- Bioinformatics and Genomic Networks 6
- Gene expression and cancer classification 5
- Machine Learning in Bioinformatics 2
- Genomics and Chromatin Dynamics 1
- Microbial Metabolic Engineering and Bioproduction 1
- Oncology 2
- Cancer Risks and Factors 1
- Co-authors
- Sabah Jassim (5 shared papers)Michael A. Cawthorne (5 shared papers)Kenneth Langlands (5 shared papers)Jemma C. Hopewell (2 shared papers)Federico Murgia (2 shared papers)Francesco Viola (1 shared paper)Bernard M. Schuman (1 shared paper)Sachin Batra (1 shared paper)
- Journals
- Journal of Computational Biology (1 paper)PLoS Medicine (1 paper)Cancer Research (1 paper)Hypertension (1 paper)BMC Bioinformatics (1 paper)
- Partner nations
- United KingdomUnited StatesIndia
In The Last Decade
M Ibrahim
8 papers receiving 86 citations
Peers
Comparison fields: 5 of 34
- Molecular Biology 48
- Computational Theory and Mathematics 8
- Nephrology 3
- Biological Psychiatry 1
- Cardiology and Cardiovascular Medicine 8
Countries citing papers authored by M Ibrahim
This map shows the geographic impact of M Ibrahim'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 M Ibrahim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Ibrahim more than expected).
Fields of papers citing papers by M Ibrahim
This network shows the impact of papers produced by M Ibrahim. 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 M Ibrahim. The network helps show where M Ibrahim may publish in the future.
Co-authors
The 25 scholars most cited alongside M Ibrahim, 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 | 2012 | 36 | |
| 2 | 2022 | 15 | |
| 3 | 2021 | 11 | |
| 4 | 2014 | 8 | |
| 5 | Fiberoptic sigmoidoscopy in screening pattern makers for colon cancer at their work place. | 1984 | 8 |
| 6 | 2011 | 4 | |
| 7 | 2011 | 3 | |
| 8 | 2018 | 1 | |
| 9 | 2019 | 0 | |
| 10 | 2012 | 0 |
About M Ibrahim
M Ibrahim is a scholar working on Molecular Biology, Oncology, Surgery, Pathology and Forensic Medicine and Radiology, Nuclear Medicine and Imaging, having authored 10 papers that have together received 86 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (6 papers), Gene expression and cancer classification (5 papers), Machine Learning in Bioinformatics (2 papers), Genomics and Chromatin Dynamics (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Breast Cancer Treatment Studies (1 paper), Microbial Metabolic Engineering and Bioproduction (1 paper) and Cancer Risks and Factors (1 paper). The work is most often cited by research in Molecular Biology (48 citations), Computational Theory and Mathematics (8 citations), Nephrology (3 citations), Biological Psychiatry (1 citation) and Cardiology and Cardiovascular Medicine (8 citations). M Ibrahim has collaborated with scholars based in United Kingdom, United States and India. Frequent co-authors include Sabah Jassim, Michael A. Cawthorne, Kenneth Langlands, Jemma C. Hopewell, Federico Murgia, Francesco Viola, Bernard M. Schuman, Sachin Batra, Alexander Stiby and Parag Gajendragadkar. Their work appears in journals such as Journal of Computational Biology, PLoS Medicine, Cancer Research, Hypertension and BMC Bioinformatics.
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.