Understanding the origins and growth of cancer requires understanding the role of genetics in encoding proteins that form phenotypes and molecular alterations at multiple levels (e.g., gene, cell, and tissue). Tumors, for example, undergo dynamic spatio-temporal changes, both during their progression and in response to therapies. Therefore, there is a pressing need to design and develop mathematical and computational strategies to harness cancer data in an accurate and efficient fashion. Advanced mathematical and computational models could provide the tools to make therapeutic strategies adaptable enough and to address the emerging targets. Similarly, understanding the interrelationship amongst complex biological processes requires analyzing very large databases of cellular pathways. High-performance computing, big data analytics, data-intensive computing, machine learning, artificial intelligence, and medical image analysis techniques could be critical in addressing these challenges.

ISMCO seeks papers describing contributions to the state of the art and practice in mathematical and computational oncology. Topics of interest include, but not limited, the following areas:

Topics

Multiscale advanced mathematical and computational models
Precision medicine and immuno-oncology
Spatio-temporal tumor modeling and simulation
Tumor forecasting methods
Molecular subtyping, survival analysis and prediction
Novel experimental cultures
Cancer genomics and proteomics
Next-generation sequencing and single-cell analysis
Systems biology and networks
General cancer computational biology
Computational methods for anticancer drug development
Cancer epidemiology, biomarkers and prevention
Statistical methods and data mining for cancer research
Deep learning and machine learning for cancer research
Big data analytics for cancer research
High performance computing for cancer research
Data intensive computing for cancer research
Scalable and high throughput systems for large-scale cancer-data analytics
Text analytics and natural language processing (NLP) for cancer research
Automatic semantic annotation of medical content in the context of cancer disease
Application of cloud computing, SaaS and PaaS architectures for cancer research
Computer-aided diagnosis (CADx) systems for cancer research
Computer vision, scientific visualization, and image processing for cancer research
Robotics for cancer research