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ISMCO 2020: International Symposium on Mathematical and Computational Oncology

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截稿日期:
2020-08-31 Extended
通知日期:
2020-09-21
会议日期:
2020-10-08
会议地点:
San Diego, California, USA
届数:
2
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征稿

ISMCO 2020 (International Symposium on Mathematical and Computational Oncology) is an academic conference held in San Diego, California, USA on 2020-10-08. The paper submission deadline is 2020-08-31 (extended). Acceptance notifications are sent on 2020-09-21.

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
由 Dou Sun 最后更新于

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