Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Forensic Scientist

Assist. Prof. Dr. Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

Anhui University of Chinese Medicine | China

Dr. Yinfeng Yang is a distinguished researcher specializing in artificial intelligence, bioinformatics, and traditional Chinese medicine (TCM), with a strong emphasis on AI-enabled drug discovery and biomedical big data analysis. Her work integrates multi-omics analytics, computational modeling, and machine learning to uncover disease biomarkers, elucidate therapeutic mechanisms, and accelerate the discovery of bioactive compounds from natural medicines. Dr. Yinfeng Yang has made significant contributions to AI-assisted drug discovery (AIDD), advancing methodologies in molecular docking, molecular dynamics simulations, high-throughput virtual screening, and quantitative structure–activity relationship modeling. She has authored 44 Scopus-indexed publications, accumulating 5,663 citations across 5,524 documents, supported by a Scopus h-index of 18, underscoring her impact in computational biology and integrative medicine research. Her studies, published in leading journals such as Journal of Advanced Research, Phytomedicine, ACS Omega, Drug Discovery Today, and Current Medicinal Chemistry, span diverse topics including cancer prognosis modeling, multi-scale mechanisms of herbal medicine, ADMET prediction frameworks, and the therapeutic potential of Ginkgo biloba in oncology and neurological disorders. Dr. Yinfeng Yang has led and contributed to numerous scientific research projects focused on TCM modernization, biomedical intelligence, and compound drug discovery. She also plays an active role in scholarly publishing as an editorial board member for journals such as PLOS ONE and Journal of Hebei Medical University, and serves as a recognized reviewer for more than 30 international journals, including Nature Communications, Phytomedicine, ACS Omega, and Journal of Ethnopharmacology. Her research excellence continues to advance innovation in AI-driven precision medicine and the global understanding of natural-product-based therapeutics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

  • Fan, N., Chen, J., Wang, J., Chen, Z. S., & Yang, Y. (2025). Bridging data and drug development: Machine learning approaches for next-generation ADMET prediction. Drug Discovery Today, Article 104487.

  • Han, Z., Liu, Q., Yang, J., Wang, X., Song, W., Wang, J., & Yang, Y. (2025). Exploration of the mechanism of Ginkgo biloba leaves targeted angiogenesis against gastric cancer. ACS Omega, 10(35), 40460–40476.

  • Li, H., Fu, S., Shen, P., Zhang, X., Yang, Y., & Guo, J. (2025). Mitochondrial pathways in rheumatoid arthritis: Therapeutic roles of traditional Chinese medicine and natural products. Phytomedicine, Article 157106.

  • Yang, P., Wang, X., Yang, J., Yan, B., Sheng, H., Li, Y., Yang, Y., & Wang, J. (2025). AI-driven multiscale study on the mechanism of Polygonati Rhizoma in regulating immune function in STAD. ACS Omega, 10(19), 19770–19796.

  • Zhang, H., Xu, Q., Kan, H., Yang, Y., & Cai, Y. (2025). Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer. Discover Oncology, 16(1), 381.

Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Published on by Forensic Scientist

Assoc. Prof. Dr. Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Dr. Mahmoud Khaled Abd-Ellah is an accomplished Assistant Professor at the Faculty of Artificial Intelligence, Egyptian Russian University, Badr, Egypt, widely recognized for his pioneering research at the intersection of artificial intelligence, medical imaging, and deep learning. Holding a Ph.D. in Electrical Engineering from Minia University, his doctoral research focused on brain tumor diagnosis through MRI using advanced machine learning techniques. His impressive publication portfolio includes 22 Scopus-indexed papers, collectively cited 830 times by 774 documents, with an h-index of 12 demonstrating substantial scientific impact and research excellence. His scholarly work has been featured in leading journals such as Scientific Reports, Neural Computing and Applications, and Ecological Informatics, advancing AI-driven approaches for medical image analysis, automated brain tumor detection, COVID-19 classification, and environmental data modeling. Beyond research, Dr. Abd-Ellah actively contributes to academic governance and quality enhancement as a member of the Egyptian International Ranking Committee, head of the Quality Management Unit, and ranking official at the Egyptian Russian University. He is also an active member of multiple IEEE councils, engaging in the development and application of AI across engineering and biomedical domains. His ORCID profile (0000-0002-6840-2503) and Scopus ID (57191265348) further reflect his consistent record of impactful scholarship and international collaboration. With his interdisciplinary expertise, editorial service, and mentorship of Ph.D. and master’s students, Dr. Mahmoud Khaled Abd-Ellah exemplifies academic leadership, innovation, and a transformative approach to research that advances both science and society.

Profile: Scopus | Google Scholar | ORCID | ResearchGate

Featured Publications

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019). A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging, 61, 300–318.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 1–10.

  • El-Rawy, M., Abd-Ellah, M. K., Fathi, H., & Ahmed, A. K. A. (2021). Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques. Journal of Water Process Engineering, 44, 102380.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2016). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM) (pp. 73–76).

  • MostafaShokry, A. A. M. K., Awad, A. I., & Abd-Ellah, M. K. (2022). Systematic survey of advanced metering infrastructure security: Vulnerabilities, attacks, countermeasures, and future vision. Future Generation Computer Systems, 1–21.