Zainal Ariff Abdul Rahman
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Zainal Ariff Abdul Rahman's research focuses on cancer, particularly oral squamous cell carcinoma, and related genetic disorders. He has investigated potential glycoprotein biomarkers in oral squamous cell carcinoma and conducted comparative sera proteomics analysis to identify differentially expressed proteins in this disease. His work also includes studying the overexpression of gamma glutamyl hydrolase as a predictor of extranodal extension in oral cavity squamous cell carcinoma.
Abdul Rahman has also explored therapeutic strategies, including DNA vaccines targeting cancer-associated antigens for head and neck cancers in conjunction with checkpoint inhibitors. His research interests extend to genetic therapies for conditions like Apert syndrome, involving systematic reviews of insights and future directions. Additionally, he has contributed to the fabrication of a three-dimensional temporomandibular joint model for surgical simulation and has examined ophthalmic features of craniosynostosis.
With a career marked by productivity, Abdul Rahman has authored over 100 publications and maintains an h-index of 25, with more than 1,500 citations. He is recognized as a highly cited researcher, indicating a significant impact within his fields of study.
Metrics
- h-index: 25
- Publications: 102
- Citations: 1,594
Selected Publications
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Species-Specific Protein Function Prediction in Flavobacterium covae Using Ensemble Machine Learning (2025)
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Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i> (2025)
Collaboration Network
Top Collaborators
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Species-Specific Protein Function Prediction in Flavobacterium covae Using Ensemble Machine Learning
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Species-Specific Protein Function Prediction in Flavobacterium covae Using Ensemble Machine Learning
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Machine Learning and Network Analysis to Predict Hypothetical Protein Functions of Aeromonas <i>hydrophila</i>
- Species-Specific Protein Function Prediction in Flavobacterium covae Using Ensemble Machine Learning
- Species-Specific Protein Function Prediction in Flavobacterium covae Using Ensemble Machine Learning
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