Roghayyeh Alipanahi | Computational Biology | Best Researcher Award

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Dr. Roghayyeh Alipanahi | Computational Biology | Best Researcher Award

Postdoctoral at Zanjan university | Iran

Dr. Roghayyeh Alipanahi is a Ph.D. candidate in Artificial Intelligence at the University of Zanjan, specializing in bioinformatics, genome editing, and deep learning. Her research focuses on advancing CRISPR and prime editing technologies through computational approaches, particularly by applying machine learning and neural networks to predict editing efficiency and off-target effects. With strong expertise in AI, bioinformatics, and pattern recognition, she has published impactful studies in leading journals such as Molecular Therapy – Nucleic Acids and Frontiers in Bioinformatics.

Professional Profile:

Education: 

Dr. Roghayyeh Alipanahi is currently pursuing her Doctor of Philosophy (Ph.D.) in Artificial Intelligence at the University of Zanjan, where her doctoral thesis focuses on predicting the accuracy of CRISPR-based genome editing using deep learning models, particularly CNN and BiLSTM architectures. She holds a Master’s degree in Information Technology from the Amir Kabir University of Technology – Tehran Polytechnic, where she strengthened her expertise in computational systems and applied technologies. She completed her Bachelor’s degree in Information Technology at the Azarbaijan University of Tarbiat Moallem, laying the foundation for her career in artificial intelligence and bioinformatics.

Experience:

Dr. Roghayyeh Alipanahi has built a strong academic and professional career combining teaching, research, and IT practice. She has been serving as an Artificial Intelligence Engineer (Teaching Assistant) at the University of Zanjan, contributing to research and instruction in AI-driven computational biology. In parallel, she has been working as an IT Specialist at the Welfare Organization in Tabriz, Iran, providing technical expertise and digital solutions. She also shares her knowledge as a Teacher at Shahid Madani, Tabriz and as a Lecturer at Islamic Azad University (IAU), Tabriz, where she teaches and mentors students in artificial intelligence, machine learning, and information technology.

Research Interest:

  • CRISPR/Cas9 and Prime Editing – developing predictive models for genome editing efficiency and off-target activity.

  • Deep Learning and Machine Learning – applying CNNs, BiLSTM, and transformers to biological data.

  • Bioinformatics – computational analysis of genetic sequences and editing outcomes.

  • Pattern Recognition & NLP – applications in computational biology and AI-based modeling.

  • Image Processing – applying AI to medical and biological imaging.

Publications Top Noted:

  • Advancing CRISPR with Deep Learning: A Comprehensive Review of Models and Databases – 2025

  • DTMP-prime: A Deep Transformer-Based Model for Predicting Prime Editing Efficiency and PegRNA Activity – 2024

  • CRISPR Genome Editing Using Computational Approaches: A Survey – 2023

  • New Encoding Model Based on gRNA-DNA Pairs to Predict Off-Target Effects of Genome Editing with CRISPR/Cas9 – 2023

Conclusion:

Dr. Roghayyeh Alipanahi’s pioneering work in AI-driven genome editing and bioinformatics makes her an outstanding candidate for the Best Researcher Award in Medical Technology. Her contributions significantly advance computational solutions for CRISPR and prime editing, offering critical tools for the future of genetic medicine. By expanding her international collaborations and bridging computational predictions with experimental biology, she is well-positioned to become a global leader in medical biotechnology research. Her achievements align with the award’s mission to recognize researchers driving transformative, interdisciplinary, and impactful medical technologies.

Dr. Liangrui Pan | Bioinformatics | Best Researcher Award

Published on by Network Awards

Dr. Liangrui Pan | Bioinformatics | Best Researcher Award

Dr. Liangrui Pan, Hunan university, China

Dr. Liangrui Pan is an emerging scholar in the field of computer science, currently pursuing a Ph.D. at Hunan University, China. With a Master’s Degree in Computer Science from Prince of Songkla University, Thailand, his research interests encompass machine learning, deep learning, and pattern recognition. As a dedicated professional, Dr. Pan is actively involved in various professional organizations, holding memberships in the Chinese Society of Electrical Engineering, the IEEE Power and Energy Society, and the China Computer Federation. His engagement with these communities highlights his commitment to advancing the field and contributing to the broader scientific discourse.

🌐 Professional Profile:

Google Scholar

Orcid

Education

  • Ph.D. in Computer Science (Ongoing)
    • Hunan University, Changsha, China
  • Master’s Degree in Computer Science (2021)
    • Prince of Songkla University, Thailand

Research Interests

  • Machine Learning
  • Deep Learning
  • Pattern Recognition

Professional Memberships

  • Member of the Chinese Society of Electrical Engineering
  • Member of the IEEE Power and Energy Society
  • Member of the China Computer Federation

Professional Activities

  • IEEE Power and Energy Society: Beijing, China
    • Membership
  • China Computer Federation: Beijing, China
    • Membership

Publication Top Notes:

  1. Title: A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
    • Journal: Journal of Raman Spectroscopy
    • Citations: 42
    • Year: 2022
  2. Title: Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
    • Journal: Biomedical Signal Processing and Control
    • Citations: 29
    • Year: 2022
  3. Title: MFDNN: multi-channel feature deep neural network algorithm to identify COVID-19 chest X-ray images
    • Journal: Health Information Science and Systems
    • Citations: 16
    • Year: 2022
  4. Title: Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network
    • Journal: IEEE Sensors Journal
    • Citations: 16
    • Year: 2021
  5. Title: Hybrid Beamforming based on an Unsupervised Deep Learning Network for Downlink Channels with Imperfect CSI
    • Journal: IEEE Wireless Communications Letters
    • Citations: 13
    • Year: 2022