Guest Editors
Prof. Guanhu Yang
Email: gy182915@ohio.edu
Affiliation: Department of Specialty Medicine, Heritage College of Osteopathic Medicine, Ohio University 191 W. Union Street Athens, OH 45701, USA
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Research Interests: urologic diseases, multi-omics, integrative medicine, targeted therapy, tumor immunology
Dr. Ke Xu
Email: cqghxuke@cqu.edu.cn
Affiliation: Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China
Homepage:
Research Interests: bioinformatics, big data analytics, machine learning, artificial intelligence, tumor markers, tumor immunity
Summary
Big data analytics has emerged as a transformative force in modern medicine, offering unparalleled opportunities to enhance our understanding of urological diseases, improve diagnostic accuracy, and optimize treatment strategies. The integration of diverse data sources—ranging from genomics, proteomics, and metabolomics to crucial clinical records, imaging data, and real-world evidence—and crucially, the support of findings with experimental or clinical validation, enables the identification of novel biomarkers, prediction of disease progression, and development of personalized therapeutic approaches with actionable insights and verifiable discoveries. This Special Issue aims to showcase cutting-edge research that highlights the potential of big data in revolutionizing the diagnosis and treatment of urological diseases and emphasizes studies that integrate and validate big data findings with experimental or clinical data to strengthen translational significance and provide practical value for clinical decision-making and patient outcomes.
We invite submissions that explore innovative applications of big data analytics, including machine learning, artificial intelligence, and network-based approaches, to address key challenges in urology.
This Special Issue will cover a broad range of topics related to the application of big data in urology, including but not limited to:
· Innovative Approaches in Big Data Analytics for Urological Disease Diagnosis
· Multi-Omics Integration in Urological Diseases
· Predictive Models for Treatment Response in Urology
· Personalized Treatment Strategies for Urological Conditions
· Artificial Intelligence in Urological Clinical Decision-Making
· Biomarker Discovery through Big Data in Urological Diseases
Keywords
big data analytics,urological diseases,precision medicine,multi-omics integration,machine learning,personalized treatment,biomarker discovery
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