Integrating Complex Systems, Statistical Modeling, Machine Learning, and AI in Cancer Research and Survival Analysis

Cancer research and survival analysis present profound challenges due to the intricate, multifactorial nature of cancer development, progression, and treatment response. The complexity of biological, clinical, and environmental interactions calls for advanced methodologies capable of capturing non-linearity, emergent behaviors, feedback loops, and adaptive dynamics. The integration of complex systems approaches with statistical modeling, machine learning, and artificial intelligence (AI) opens new frontiers for understanding these complexities and improving predictive and prescriptive capabilities.

This Research Topic aims to explore the interrelation of complex systems theory, statistical methods, machine learning, and AI in cancer research and survival analysis, emphasizing both theoretical innovations and practical implementations. We invite contributions that investigate how combining these methodologies can enhance the understanding of cancer mechanisms, improve prognostic models, optimize treatment strategies, and ultimately advance patient survival outcomes. Topics of interest include, but are not limited to:
· Hybrid approaches integrating complex systems modeling with statistical and AI techniques
· Advanced machine learning algorithms for survival prediction and risk stratification
· Network science and graph-based learning in cancer genomics and molecular interactions
· Systems dynamics and AI-driven feedback analysis in cancer treatment and management
· Multi-scale modeling combining statistical inference and machine learning
· Applications of chaos theory and non-linear dynamics enhanced by AI methodologies
· Robustness and resilience analysis in cancer treatment systems through data-driven approaches
· Computational approaches to personalized oncology using AI and complex systems modeling
· Case studies demonstrating the synergy of complex systems, statistical methods, and AI in cancer research and survival analysis

Through this Research Topic, we seek to bridge the gap between complexity science, advanced statistical modeling, and AI-driven techniques in oncology practice, fostering innovative solutions to contemporary challenges in cancer research and patient care. We encourage interdisciplinary contributions from researchers and practitioners in fields such as systems biology, computational oncology, health informatics, data science, and clinical research.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Cancer research, Machine learning, Predictive modeling, Complex systems theory, Multi-scale modeling

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.