N
Nazar Zaki
Researcher at College of Information Technology
Publications - 139
Citations - 2095
Nazar Zaki is an academic researcher from College of Information Technology. The author has contributed to research in topics: Protein sequencing & Image segmentation. The author has an hindex of 20, co-authored 132 publications receiving 1391 citations. Previous affiliations of Nazar Zaki include Universiti Teknologi Malaysia & United Arab Emirates University.
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Protein complex detection using interaction reliability assessment and weighted clustering coefficient
TL;DR: A novel graph mining algorithm (PEWCC) is proposed to identify protein complexes based on the concept of weighted clustering coefficient and was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores.
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Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review.
TL;DR: It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity, and other comorbidities, such as cancer, kidney disease, and stroke must be further evaluated to determine a strong relationship to the virus.
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Saffron-Based Crocin Prevents Early Lesions of Liver Cancer: In vivo, In vitro and Network Analyses
Amr Amin,Alaaeldin A. Hamza,Sayel Daoud,Kamal Khazanehdari,Ala’a Al Hrout,Badriya Baig,Amphun Chaiboonchoe,Thomas E. Adrian,Nazar Zaki,Kourosh Salehi-Ashtiani +9 more
TL;DR: Findings introduced crocin as a candidate chemopreventive agent against HCC by demonstrating the anti-proliferative and pro-apoptotic properties of crocin when administrated in induced- HCC model.
Posted ContentDOI
Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: a systematic review
TL;DR: It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity, and other comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus.
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A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques
TL;DR: The review results indicated that various Machine Learning techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction and improving the students’ performance.