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PublicationShort-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units( 2024)
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PublicationA machine learning method for the identification and characterization of novel COVID-19 drug targets( 2023-05-03)
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PublicationUncovering Chains of Infections through Spatio-Temporal and Visual Analysis of COVID-19 Contact Traces( 2022)
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PublicationOverview with details for exploring geo-located graphs on maps( 2016)
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PublicationThe technologically integrated oncosimulator: Combining multiscale cancer modeling with information technology in the in silico oncology context( 2014)This paper outlines the major components and function of the Technologically Integrated Oncosimulator developed primarily within the ACGT (Advancing Clinico Genomic Trials on Cancer) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity - discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid and visualization of the predictions. A refining scenario for the eventual coupling of the Oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the Oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.
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PublicationOpening up the "black box" of medical image segmentation with statistical shape models( 2013)
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PublicationSpace-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns( 2010)Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the "Self-Organizing Map" (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA.
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PublicationA framework for using self-organizing maps to analyze spatio-temporal patterns, exemplified by analysis of mobile phone usage( 2010)We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios.
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PublicationDesign and narrative structure for the virtual human scenarios( 2007)This article describes the design of the two application scenarios of the Virtual Human project and its integration into the Virtual Human system. This includes overall concepts and considerations of the demonstrators for the two application scenarios (learning, edutainment) as well as underlying methodic-didactic aspects for knowledge transmission and narrative concepts for story structure and story control during run-time of the system. Hence, in contrast to traditional learning systems with virtual characters as virtual instructors, an exciting and suspenseful interactive information space has been created. On the one hand, the methodic-didactic methods and VH learning model guarantee learning effects, on the other hand narrative structures and an emotion module provide the ground for a playful and exciting story environment, whereby the users can interact and discuss with a set of virtual characters.
