About this Research Topic
Cancer is not a single disease, it is a complex and heterogeneous disease which leads to the second cause of death worldwide. Although all cancers manifest themselves as an uncontrolled growth of abnormal cells, they are actually distinct neoplastic diseases that possess different genetic and epigenetic alterations, underlying molecular mechanisms, histopathologies and clinical outcomes. 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).
Despite significant advances in the understanding of the principal mechanisms leading to various cancer types, less progress has been made toward developing patient-specific treatments. Advanced mathematical and computational models could play a significant role in examining the most effective patient-specific therapies. This research topic solicits papers dealing with all aspects of mathematical and computational oncology including but not limited to: multiscale advanced mathematical and computational models, precision medicine, immuno-oncology, spatio-temporal tumor modeling and simulation, tumor forecasting, cancer genomics and proteomics, next-generation sequencing and single-cell analysis, anticancer drug development, systems biology and networks, general cancer computational biology, statistical methods, big data, deep learning, high performance computing, cloud computing, scientific visualization, image processing, computer vision, robotics, and natural language processing.
This Research Topic solicits papers describing contributions to the state of the art and practice in mathematical and computational oncology. Authors are also encouraged to present their work at the ISMCO 2020, International Symposium on Mathematical and Computational Oncology, October 8-10, 2020, Virtually.
Topics of interest include:
• 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
• Computer-aided diagnosis (CADx) systems for research
• 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
• Artificial Intelligence for cancer research
The Topic Editors George Bebis, Heyrim Cho, David A Hormuth II, Max A. Alekseyev, Jana Gevertz and Maria Rodriguez Martinez declare that they are affiliated with the ISMCO 2020 conference on Mathematical and Computational Oncology.
Keywords: tumor modeling, cancer genomics, big data, cancer biology, precision medicine
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.