| Literature DB >> 24808225 |
Vangelis Sakkalis, Stelios Sfakianakis, Eleftheria Tzamali, Kostas Marias, Georgios Stamatakos, Fay Misichroni, Eleftherios Ouzounoglou, Eleni Kolokotroni, Dimitra Dionysiou, David Johnson, Steve McKeever, Norbert Graf.
Abstract
Significant Virtual Physiological Human efforts and projects have been concerned with cancer modeling, especially in the European Commission Seventh Framework research program, with the ambitious goal to approach personalized cancer simulation based on patient-specific data and thereby optimize therapy decisions in the clinical setting. However, building realistic in silico predictive models targeting the clinical practice requires interactive, synergetic approaches to integrate the currently fragmented efforts emanating from the systems biology and computational oncology communities all around the globe. To further this goal, we propose an intelligent graphical workflow planning system that exploits the multiscale and modular nature of cancer and allows building complex cancer models by intuitively linking/interchanging highly specialized models. The system adopts and extends current standardization efforts, key tools, and infrastructure in view of building a pool of reliable and reproducible models capable of improving current therapies and demonstrating the potential for clinical translation of these technologies.Entities:
Mesh:
Year: 2014 PMID: 24808225 DOI: 10.1109/JBHI.2013.2297167
Source DB: PubMed Journal: IEEE J Biomed Health Inform ISSN: 2168-2194 Impact factor: 5.772