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