Dr. Maha Thafar | Data mining | Best Researcher Award

Assistant Professor, Taif University, Saudi Arabia

 

Dr. Maha Thafar is an Assistant Professor at Taif University, Saudi Arabia. She specializes in artificial intelligence, machine learning, data science, and bioinformatics. Dr. Thafar earned her B.S. in computer science from King Abdulaziz University and her M.S. from Kent State University. She completed her Ph.D. at King Abdullah University of Science and Technology (KAUST), focusing on computational methods for bioinformatics. Her research aims to apply AI and machine learning techniques to biomedical and healthcare domains, with a special interest in drug repositioning.

Profile

Orcid

Education

📚 Ph.D. in Computer Science (Bioinformatics), King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2016 – present)
Dr. Thafar’s doctoral research at KAUST’s Computational Bioscience Research Center focuses on developing AI-driven methods for bioinformatics applications.

📖 Master of Science in Computer Science, Kent State University, USA (2013 – 2015)
During her master’s studies, Dr. Thafar honed her skills in computer science, emphasizing machine learning and data analysis.

🎓 Bachelor of Science in Computer Science, King Abdulaziz University, Saudi Arabia
Her undergraduate education laid the foundation for her expertise in computer science and bioinformatics.

Experience

👩‍🏫 Assistant Professor, Taif University, Saudi Arabia (June 2022 – present)
Dr. Thafar is engaged in teaching and research in the College of Computers and Information Technology, where she integrates her AI and bioinformatics knowledge into her curriculum.

🎓 Ph.D. Student, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (August 2016 – present)
While pursuing her doctorate, she has been deeply involved in cutting-edge research, focusing on bioinformatics and computational biosciences.

Research Interests

🔬 Dr. Thafar’s research interests encompass the development of computational methods using artificial intelligence, machine learning, and data and graph mining. She applies these techniques to biomedical and healthcare domains, particularly drug repositioning, which aims to find new therapeutic uses for existing drugs.

Publications Top Notes

Dr. Thafar has a rich portfolio of research publications, many of which are highly cited and published in prestigious journals. Here are some notable publications:

1. FutureCite: Predicting Research Articles’ Impact Using Machine Learning and Text and Graph Mining Techniques (2024), Mathematical and Computational Applications
   Cited by: This article explores the prediction of research impact using advanced machine learning techniques.
2. OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features (2023), Frontiers in Genetics
   Cited by: This research utilizes BERT embeddings to identify new therapeutic targets in oncology.
3. Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets (2023), Scientific Reports
   Cited by: A study leveraging machine learning to uncover biomarkers and targets for Alzheimer’s disease.
4. VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants (2023), Briefings in Bioinformatics
   Cited by: This paper introduces a deep learning method for predicting genetic variant functions.
5. Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications (2022), PeerJ
   Cited by: An innovative approach combining knowledge graphs and text for drug interaction predictions.