Lecturer at Razi University, Iran
Dr. Shima Shafiee is an Iranian researcher specializing in artificial intelligence, bioinformatics, and computational biology. She has demonstrated remarkable expertise in applying machine learning models to complex biomedical problems, particularly in protein-peptide interaction prediction. With a solid foundation in computer engineering and years of academic and research experience, she has developed predictive models and hybrid algorithms that bridge the gap between computer science and life sciences. Dr. Shafiee’s extensive publication record in internationally recognized journals, her involvement in teaching and interdisciplinary research, and her dedication to lifelong learning and professional development highlight her as an influential and forward-looking scholar. She is currently engaged in postdoctoral research and academic roles, contributing to AI-driven solutions in healthcare and medical data analysis. Her collaborative efforts, teaching experience, and scientific contributions position her as a promising leader in both national and international research communities.
Professional Profile
Education🎓
Dr. Shima Shafiee holds a Ph.D. in Computer Engineering with a specialization in Computer Systems Architecture from Razi University, where her dissertation focused on learning-based models for predicting protein-peptide binding interactions. Prior to this, she completed her M.Sc. in Computer Science from Tabari University, concentrating on optimization algorithms for the two-dimensional bin packing problem. She earned her B.Sc. in Computer Engineering from Kerman University, with a focus on the role of information technology in cybercrime and money laundering. Her academic path has been marked by a strong interdisciplinary orientation, merging principles of algorithm design, artificial intelligence, and systems engineering. Dr. Shafiee is currently preparing to begin her postdoctoral research at Shahid Bahonar University of Kerman, focusing on advanced machine learning applications in medical image analysis and bioinformatics. Her academic training reflects a rigorous and innovative approach to solving computational challenges in the biological and medical sciences.
Professional Experience📝
Dr. Shima Shafiee has accumulated a broad range of academic and research experience over the years. She has served as a lecturer at Razi University and Shahid Bahonar University of Kerman, teaching subjects such as artificial intelligence, computer programming, fundamentals of computing, and algorithm design. Her professional journey includes roles as a research assistant and educational collaborator on interdisciplinary projects in bioinformatics and medical imaging. She has participated in collaborative initiatives with departments in law and medical sciences, highlighting her cross-disciplinary competence. Dr. Shafiee’s earlier professional activities include internships, secondary school teaching, and work as an educational researcher at Tarbiat Modares University. Beyond academia, she actively delivers presentations and training sessions on AI tools in education, medical science, and government applications. Her career reflects a continuous evolution from foundational computing education to advanced AI-driven research and applications in healthcare and biotechnology.
Research Interest🔎
Dr. Shima Shafiee’s research interests center on artificial intelligence applications in bioinformatics, particularly the prediction and analysis of protein-peptide and protein-protein interactions. Her work leverages machine learning, deep learning, ensemble models, and evolutionary computation to develop predictive tools with real-world implications in biomedical science. She is passionate about creating models that improve the accuracy and efficiency of residue-level interaction predictions, utilizing sequence- and structure-based features. Recently, her research has expanded into medical image processing, AI-based diagnostic support systems, and applications of large language models in biological data analysis. She is also actively exploring segmentation-based algorithms and attention mechanisms for bioinformatic tasks. Her interdisciplinary approach connects computer science, structural biology, and clinical applications, aiming to provide computational insights that aid in drug discovery, diagnostics, and personalized medicine. Dr. Shafiee’s ongoing work represents the forefront of AI-driven solutions in life sciences.
Award and Honor🏆
Throughout her academic career, Dr. Shima Shafiee has received several accolades in recognition of her scholarly excellence and scientific contributions. She was recognized as the top-performing student during her Ph.D. studies and earned third place in her M.Sc. program. One of her early works was selected among the best papers at the 2nd International Congress of Electrical Engineering, Computer Science, and Information Technology. Her research papers have been accepted at notable national and international conferences, and her interdisciplinary contributions have been acknowledged by academic bodies. She has also served as a reviewer for various scientific journals and conferences, including IEEE-related events and bioinformatics journals. Dr. Shafiee is an active member of multiple academic and scientific societies, which reflects her standing within the research community. Her dedication to advancing computational applications in biomedicine has earned her a respected position in her field.
Research Skill🔬
Dr. Shima Shafiee possesses a diverse and well-rounded research skill set, spanning programming, algorithm development, data modeling, and AI tool integration. She is proficient in Python, R, WEKA, SPSS, MATLAB, and BioPython, among other platforms, and applies these tools in both supervised and unsupervised machine learning environments. Her work includes the development of hybrid models combining genetic programming, deep learning, support vector machines, and ensemble techniques for high-precision biomedical predictions. Dr. Shafiee is also skilled in optimization algorithms, having previously worked on solving the two-dimensional bin packing problem using particle swarm optimization. She integrates her technical expertise with domain knowledge in protein chemistry, structural biology, and medical imaging to produce interdisciplinary solutions. In addition to computational methods, she is experienced in academic writing, peer reviewing, and scientific communication. Her technical and analytical capabilities are well-aligned with emerging challenges in bioinformatics and artificial intelligence.
Conclusion💡
Dr. Shima Shafiee is a highly deserving candidate for the Best Researcher Award due to her exceptional contributions to the fields of computational biology, artificial intelligence, and bioinformatics. Her innovative research on protein-peptide interaction prediction, coupled with a strong publication record in prestigious journals and conferences, reflects both scientific rigor and societal relevance, particularly in advancing biomedical research. With her interdisciplinary focus, commitment to academic excellence, and proactive engagement in teaching, reviewing, and professional development, Dr. Shafiee exemplifies the qualities of a forward-thinking researcher. As she continues to expand her work into medical image processing and international collaborations, she holds immense potential for future leadership and transformative impact in science and technology.
Publications Top Noted✍
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Title: SPPPred: Sequence-based protein-peptide binding residue prediction using genetic programming and ensemble learning
Authors: S. Shafiee, A. Fathi, G. Taherzadeh
Year: 2022
Citations: 12
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Title: Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms
Authors: S. Shafiee, A. Fathi
Year: 2021
Citations: 6
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Title: Combination of genetic programming and support vector machine-based prediction of protein-peptide binding sites with sequence and structure-based features
Authors: S. Shafiee, A. Fathi
Year: 2021
Citations: 6
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Title: Prediction of protein–peptide binding residues using classification algorithms
Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
Year: 2020
Citations: 6
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Title: A Review of the Uses of Artificial Intelligence in Protein Research
Authors: S. Shafiee, A. Fathi, F. Abdali-Mohammadi
Year: 2019
Citations: 5
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Title: DP-site: A dual deep learning-based method for protein-peptide interaction site prediction
Authors: S. Shafiee, A. Fathi, G. Taherzadeh
Year: 2024
Citations: 2
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Title: Protein-peptide interaction region residues prediction using a generative sampling technique and ensemble deep learning-based models
Authors: S. Shafiee, A. Fathi, G. Taherzadeh
Year: 2025
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Title: Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
Authors: S. Shafiee, A. Fathi, F. Safari
Year: 2025
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Title: Leveraging a Structure-Based and Learning-Based Predictor Using Various Feature Groups in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction)
Authors: S. Shafiee, A. Fathi
Year: 2024
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Title: Application of Learning-Based Models in Predicting of Protein-Peptide Binding Interactions
Authors: S. Shafiee
Year: 2024
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Title: Application of Combined Decision Trees Function for Optimization Issues
Authors: A. Fathi, S. Shafiee
Year: 2017