Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

• Orcid
• Google Scholar 

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

• Bioinformatics

• Machine Learning

• SNP-based Disease Prediction

• Graph Neural Networks

• DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

• 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

• Google Scholar Citations: 73

• h-index: 3

Research & Projects:

• Participated in 6 research projects, including 2 funded

• Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

• Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

• Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes:

• Optimizing Classification Efficiency with Machine Learning Techniques for Pattern Matching
  BA Hamed, OAS Ibrahim, T Abd El-Hafeez
  Journal of Big Data, 10(1), 124, 2023. [Citations: 42]

• A New Fast Technique for Pattern Matching in Biological Sequences
  OAS Ibrahim, BA Hamed, TA El-Hafeez
  The Journal of Supercomputing, 79(1), 367–388, 2023. [Citations: 20]

• A Survey on Improving Pattern Matching Algorithms for Biological Sequences
  BA Hamed, OAS Ibrahim, T Abd El-Hafeez
  Concurrency and Computation: Practice and Experience, 34(26), e7292, 2022. [Citations: 11]